706 lines
73 KiB
HTML
706 lines
73 KiB
HTML
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<h1>Agentic Engineering: A Survey of Design Patterns, Reasoning, Tool Use, Memory, Multi-Agent Coordination, Training, Safety, and Evaluation for Language Model Agents</h1>
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<p class="authors">Anonymous</p>
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<p class="affiliation">DAIR Research Notes</p>
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</header>
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<div class="abstract column-span-all">
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<p><strong>Abstract.</strong> Agentic systems built on large language models have moved rapidly from proof-of-concept demonstrations to production deployments across software engineering, scientific research, customer operations, and autonomous computer use. Their reliability depends less on raw model capability in isolation than on the engineering practice that surrounds the model: how workflows are wired, how reasoning is scaffolded, how tools are selected and invoked, how state is maintained across long horizons, how specialized agents coordinate, how agents are trained on their own trajectories, how systems behave under adversarial or misaligned conditions, and how they are evaluated on open-ended tasks. This survey frames agentic engineering as a distinct practice, organizes roughly one hundred representative works around eight substantive pillars, and draws its case material from the DAIR-AI Papers of the Week archive, a continuously updated community index spanning 2023 through 2026.</p>
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</div>
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<div class="keywords column-span-all">
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<strong>Keywords:</strong> agentic engineering, design patterns, tool use, memory, multi-agent coordination, safety, evaluation
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</div>
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<section id="sec1">
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<h2>1. Introduction</h2>
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<p>Agentic systems built on large language models have transitioned from experimental prototypes to production infrastructure across software engineering, scientific research, customer operations, and autonomous computer use. Their reliability depends less on isolated model capability than on the engineering practice that surrounds the model: how workflows are wired, how reasoning is scaffolded, how tools are selected and invoked, how state is maintained across long horizons, how specialized agents coordinate, how agents are trained on their own trajectories, how systems behave under adversarial conditions, and how they are evaluated on open-ended tasks. A large-scale field study of developer interactions confirms that professionals rely on careful planning and validation rather than unconstrained generation, and that agent suitability varies sharply by task complexity (Mueller et al., 2025). Adoption data drawn from hundreds of millions of anonymized browser interactions further shows that productivity and learning dominate usage, with adoption skewing toward higher-GDP, knowledge-intensive sectors (Harvard & Perplexity Research, 2025). These empirical findings motivate a view of agentic engineering as a distinct discipline that treats the base model as one component within a larger harness.</p>
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<p>This survey frames agentic engineering around eight substantive pillars: design patterns, reasoning and planning, tool use, memory and long-horizon control, multi-agent coordination, training and optimization, evaluation, and safety and alignment. The taxonomy reflects the observation that scaffolding can outweigh raw model performance. For instance, workflow optimization research organizes agent systems as agentic computation graphs and demonstrates that structure, search, and evaluation signals guide harness design more than marginal gains in base-model perplexity (IBM Research, 2026). Similarly, context engineering has been formalized as a discipline covering retrieval, processing, and management of information supplied to language models in production pipelines (Context Engineering Survey Team, 2025). The foundational ReAct framework established the interleaving of chain-of-thought reasoning with tool-grounded actions, creating the observe-reason-act loop that underpins most subsequent harnesses (Yao et al., 2022). Surveys of deep research agents classify systems by static versus dynamic workflows, planning strategies, and single- versus multi-agent orchestration, revealing a design space far broader than the base model alone (Deep Research Survey Authors, 2025).</p>
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<p>The survey draws its case material from the DAIR-AI Papers of the Week archive, a continuously updated community index spanning 2023 through 2026, and organizes roughly one hundred representative works across the eight pillars. Representative integrated systems illustrate the convergence of these pillars. The Confucius Code Agent, for example, combines hierarchical working memory, persistent note-taking, modular tool extensions, and a meta-agent build-test-improve loop to reach strong performance on software engineering benchmarks (Qi et al., 2026). By separating model capability from harness capability, the survey aims to clarify where investment in scaffolding, orchestration, and training yields the greatest returns for reliable agentic systems.</p>
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<p>Figure 1 presents the taxonomy that structures the remainder of this survey. The root category, Agentic Engineering, branches into the eight pillars enumerated above, each of which decomposes into concrete technical themes. Design Patterns encompasses workflows, orchestrator-workers architectures, autonomous loops, and meta-agent search. Reasoning and Planning covers chain-of-thought, deliberative search, and adaptive reasoning. Tool Use addresses function calling, Model Context Protocol interfaces, tool description optimization, and search efficiency. Memory and Long-Horizon Control includes working memory, persistent notes, context compression, and memory exposed as tools. Multi-Agent Coordination spans consensus protocols, routing and bidding, and hierarchical delegation. Training and Optimization ranges from agentic reinforcement learning to self-distillation and meta-agent discovery. Evaluation comprises general benchmarks, tool-use benchmarks, process-level verification, and empirical studies. Safety and Alignment examines offensive capability, sabotage arenas, alignment auditing, and anti-scheming training. This organization is used to navigate the literature while preserving the interdependencies that exist among pillars.</p>
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<figure class="column-span-all">
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<text x="650" y="651" font-family="Georgia" font-size="10" text-anchor="middle">General Benchmarks</text>
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<text x="650" y="679" font-family="Georgia" font-size="10" text-anchor="middle">Tool-Use Benchmarks</text>
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<text x="650" y="707" font-family="Georgia" font-size="10" text-anchor="middle">Process-Level Verification</text>
|
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<text x="650" y="735" font-family="Georgia" font-size="10" text-anchor="middle">Empirical Studies</text>
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<text x="360" y="808" font-family="Georgia" font-size="12" text-anchor="middle">Safety and Alignment</text>
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<text x="650" y="763" font-family="Georgia" font-size="10" text-anchor="middle">Offensive Capability</text>
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<rect x="500" y="776" width="300" height="22" rx="3" fill="#fbfaf6" stroke="#c0bdb0" stroke-width="0.8"/>
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<text x="650" y="791" font-family="Georgia" font-size="10" text-anchor="middle">Sabotage Arenas</text>
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<rect x="500" y="804" width="300" height="22" rx="3" fill="#fbfaf6" stroke="#c0bdb0" stroke-width="0.8"/>
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<text x="650" y="819" font-family="Georgia" font-size="10" text-anchor="middle">Alignment Auditing</text>
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<text x="650" y="847" font-family="Georgia" font-size="10" text-anchor="middle">Anti-Scheming Training</text>
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<line x1="460" y1="804" x2="500" y2="843" stroke="#1a1a1a" stroke-width="0.8"/>
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</svg>
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<figcaption>Figure 1. Taxonomy of the surveyed field. The root branches into eight pillars, each with thematic leaves that guide the literature review.</figcaption>
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</figure>
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</section>
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<section id="sec2">
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<h2>2. Design Patterns for Agentic Systems</h2>
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<p>Recurring structural patterns shape how agentic systems decompose problems and allocate computation. Early frameworks established the observe-reason-act loop, but contemporary systems increasingly rely on explicit workflow graphs. Workflow optimization research surveys methods that treat agent systems as agentic computation graphs, organizing optimization along whether structure is fixed, what is optimized, and which evaluation signals guide search (IBM Research, 2026). The ReAct framework remains a foundational primitive, interleaving chain-of-thought reasoning with actions that invoke external tools (Yao et al., 2022). Production systems such as Magentic-One instantiate an orchestrator-workers pattern in which an Orchestrator directs specialized WebSurfer, FileSurfer, Coder, and ComputerTerminal agents, yielding competitive results on general assistant benchmarks without task-specific modifications (Fourney et al., 2024). OpenDevin provides an open platform that includes sandboxed operating system and browser environments, code execution, and multi-agent support, enabling reproducible research on generalist software agents (Wang et al., 2024). OS-Copilot exposes unified operating-system-level tools and introduces FRIDAY, a self-improving agent that outperforms prior methods by a substantial margin on GAIA through file, shell, browser, and application interfaces (Wu et al., 2024).</p>
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<p>Beyond fixed pipelines, recent work automates the discovery of agent designs and unifies computation with runtime state. Automated Design of Agentic Systems proposes a meta-agent search procedure that iteratively programs and tests new agents from a growing archive, demonstrating that prompts, tool use, and control flow can all be discovered automatically (Hu et al., 2024). Neural Computers presents a unified computation-memory-IO architecture in which video models control command-line and graphical environments, blurring the boundary between model and runtime (Meta AI and KAUST, 2026). StructuredAgent introduces hierarchical planning for web agents based on dynamic AND-OR trees, with the system maintaining the global tree while the language model handles local expansion (StructuredAgent Authors, 2026). Chain-of-Agents trains single models to natively behave like multi-agent systems by distilling strong frameworks into trajectories and then running agentic reinforcement learning on verifiable web, code, and tool tasks (OPPO, 2025). Self-Organizing LLM Agents tests multi-agent autonomy at unprecedented scale across thousands of tasks and hundreds of agents, finding that agents consistently converge to similar emergent structures regardless of the coordination protocol (Self-Organizing Agents Team, 2026).</p>
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<p>Efficiency and personalization concerns have motivated architectures that separate concerns across timescales. MAPLE separates memory, learning, and personalization into specialized sub-agents operating at different timescales, yielding measurable personalization improvements (MAPLE Authors, 2026). PAHF couples explicit per-user memory with proactive and reactive feedback mechanisms for long-lived deployments (Meta Authors, 2026). Efficient Agents reviews methods for memory compression, reinforcement learning for tool use, and controlled planning mechanisms that reduce inference cost without sacrificing task success (Efficient Agents Survey Authors, 2026). The AGENTS.md evaluation study finds that human-written repository context files give only modest gains while increasing inference cost, suggesting that design patterns must be evaluated against their overhead (AGENTS.md Evaluation Team, 2026). The Agent Data Protocol unifies fragmented training datasets across tools and interfaces, enabling consolidated fine-tuning that achieves roughly twenty percent performance gains over baselines (Agent Data Protocol Team, 2025). TURA extends retrieval-augmented generation with intent-aware Model Context Protocol retrieval, DAG-based task planning, and a distilled small-model executor that matches teacher accuracy at reduced latency (TURA Authors, 2025). MACI proposes a System-2 coordination layer above language model substrates with baiting, filtering, and persistence mechanisms that enable goal-directed reasoning across multi-step tasks (MACI Authors, 2026).</p>
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<figure class="column-span-all">
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<svg width="780" height="280" viewBox="0 0 780 280" xmlns="http://www.w3.org/2000/svg">
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<text x="130" y="32" font-family="Georgia" font-size="13" font-weight="bold" text-anchor="middle">Workflow</text>
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<text x="390" y="32" font-family="Georgia" font-size="13" font-weight="bold" text-anchor="middle">Orchestrator-Workers</text>
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<text x="650" y="32" font-family="Georgia" font-size="13" font-weight="bold" text-anchor="middle">Autonomous Loop</text>
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<text x="46" y="117" font-family="Georgia" font-size="10" text-anchor="middle">Plan</text>
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<text x="128" y="117" font-family="Georgia" font-size="10" text-anchor="middle">Tool Call</text>
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<text x="87" y="207" font-family="Georgia" font-size="10" text-anchor="middle">Verify</text>
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<text x="130" y="97" font-family="Georgia" font-size="10" text-anchor="middle">Orchestrator</text>
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<text x="66" y="197" font-family="Georgia" font-size="10" text-anchor="middle">Worker A</text>
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<text x="129" y="197" font-family="Georgia" font-size="10" text-anchor="middle">Worker B</text>
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<text x="192" y="197" font-family="Georgia" font-size="10" text-anchor="middle">Worker C</text>
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<g transform="translate(563,0)">
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<text x="46" y="117" font-family="Georgia" font-size="10" text-anchor="middle">Act</text>
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<text x="128" y="117" font-family="Georgia" font-size="10" text-anchor="middle">Reflect</text>
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<text x="87" y="207" font-family="Georgia" font-size="10" text-anchor="middle">Memory</text>
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<line x1="51" y1="190" x2="92" y2="126" stroke="#1a1a1a" stroke-width="1" marker-end="url(#arrow)"/>
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</g>
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</svg>
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<figcaption>Figure 2. Three paradigms of the surveyed field. Workflow pipelines, orchestrator-workers delegation, and autonomous loops with memory represent distinct control-flow strategies.</figcaption>
|
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</figure>
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<figure class="column-span-all">
|
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<svg width="720" height="360" viewBox="0 0 720 360" xmlns="http://www.w3.org/2000/svg">
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<rect x="40" y="20" width="360" height="44" rx="4" fill="#e8e6dc"/>
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<rect x="40" y="20" width="4" height="44" fill="#8a2a2b"/>
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<text x="220" y="48" font-family="Georgia" font-size="13" font-weight="bold" text-anchor="middle">Base Model</text>
|
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<text x="420" y="42" font-family="Georgia" font-size="11" font-style="italic" fill="#6b6b6b">
|
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<tspan x="420" dy="0">Frontier or open-weight LLM</tspan>
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<tspan x="420" dy="14">with strong reasoning priors</tspan>
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</text>
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<text x="220" y="100" font-family="Georgia" font-size="13" font-weight="bold" text-anchor="middle">Reasoning and Planning</text>
|
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<text x="420" y="94" font-family="Georgia" font-size="11" font-style="italic" fill="#6b6b6b">
|
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<tspan x="420" dy="0">Chain-of-thought, deliberative</tspan>
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<tspan x="420" dy="14">search, and adaptive scaffolds</tspan>
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</text>
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<text x="220" y="152" font-family="Georgia" font-size="13" font-weight="bold" text-anchor="middle">Memory and Context</text>
|
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<text x="420" y="146" font-family="Georgia" font-size="11" font-style="italic" fill="#6b6b6b">
|
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<tspan x="420" dy="0">Working memory, persistent notes,</tspan>
|
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<tspan x="420" dy="14">and compression beyond the window</tspan>
|
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</text>
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|
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<text x="220" y="204" font-family="Georgia" font-size="13" font-weight="bold" text-anchor="middle">Tool Interface</text>
|
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<text x="420" y="198" font-family="Georgia" font-size="11" font-style="italic" fill="#6b6b6b">
|
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<tspan x="420" dy="0">Function calling, MCP servers,</tspan>
|
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<tspan x="420" dy="14">and typed executable extensions</tspan>
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</text>
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<text x="220" y="256" font-family="Georgia" font-size="13" font-weight="bold" text-anchor="middle">Orchestration Layer</text>
|
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<text x="420" y="250" font-family="Georgia" font-size="11" font-style="italic" fill="#6b6b6b">
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<tspan x="420" dy="0">Workflow graphs, multi-agent</tspan>
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<tspan x="420" dy="14">coordination, and meta-agent search</tspan>
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</text>
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<rect x="40" y="280" width="360" height="44" rx="4" fill="#fbfaf6"/>
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<text x="220" y="308" font-family="Georgia" font-size="13" font-weight="bold" text-anchor="middle">Evaluation and Observability</text>
|
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<text x="420" y="302" font-family="Georgia" font-size="11" font-style="italic" fill="#6b6b6b">
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<tspan x="420" dy="0">Benchmarks, trace capture, and</tspan>
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<tspan x="420" dy="14">process-level reliability metrics</tspan>
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</text>
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</svg>
|
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<figcaption>Figure 3. Representative stack for the surveyed field. Layers progress from base model to evaluation, with memory and tool interfaces flanking reasoning and orchestration.</figcaption>
|
|
</figure>
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|
<table class="column-span-all">
|
|
<caption>Table 1. Representative systems across the surveyed dimensions.</caption>
|
|
<thead>
|
|
<tr>
|
|
<th>System</th>
|
|
<th>Primary Pillar</th>
|
|
<th>Pattern</th>
|
|
<th>Tool or Memory Interface</th>
|
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<th>Primary Evaluation</th>
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|
</tr>
|
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</thead>
|
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<tbody>
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<tr>
|
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<td>Confucius Code Agent</td>
|
|
<td>Design + Memory</td>
|
|
<td>Orchestrator with planner and note-taker</td>
|
|
<td>Hierarchical working memory and persistent notes</td>
|
|
<td>SWE-Bench-Pro (54.3 percent Resolve@1)</td>
|
|
</tr>
|
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<tr>
|
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<td>AgentScaler</td>
|
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<td>Training</td>
|
|
<td>Forward-simulated multi-turn agent</td>
|
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<td>30k+ clustered APIs as executable functions</td>
|
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<td>tau-bench, tau2-bench, ACEBench</td>
|
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</tr>
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<tr>
|
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<td>Agentic-R1</td>
|
|
<td>Reasoning + Training</td>
|
|
<td>Adaptive text vs tool-based reasoning</td>
|
|
<td>Code execution via distilled teachers</td>
|
|
<td>DeepMath-L, Combinatorics300</td>
|
|
</tr>
|
|
<tr>
|
|
<td>TURA</td>
|
|
<td>Design</td>
|
|
<td>DAG task planner with distilled executor</td>
|
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<td>Intent-aware MCP server retrieval</td>
|
|
<td>Tool-use accuracy (88.3 percent)</td>
|
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</tr>
|
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<tr>
|
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<td>GLM-4.5</td>
|
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<td>Training</td>
|
|
<td>Hybrid thinking vs direct modes</td>
|
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<td>XML-tagged function calls</td>
|
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<td>tau-bench 70.1, BFCL-V3 77.8</td>
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</tr>
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<tr>
|
|
<td>Command A</td>
|
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<td>Training</td>
|
|
<td>Enterprise RAG plus tool-use agent</td>
|
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<td>ReAct-style function calling</td>
|
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<td>tau-bench, BFCL, enterprise generative tasks</td>
|
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</tr>
|
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<tr>
|
|
<td>ADAS (Meta Agent Search)</td>
|
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<td>Design</td>
|
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<td>Meta-agent synthesizes new agents</td>
|
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<td>Programmatic agent construction</td>
|
|
<td>Transfer across reasoning and tool-use tasks</td>
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</tr>
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<tr>
|
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<td>ActionEngine</td>
|
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<td>Tool Use</td>
|
|
<td>GUI agent as programmatic planner</td>
|
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<td>Offline state-machine exploration</td>
|
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<td>Reddit GUI tasks (95 percent, 11.8x cost reduction)</td>
|
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</tr>
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<tr>
|
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<td>SALE</td>
|
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<td>Multi-Agent</td>
|
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<td>Auction-style routing among agents</td>
|
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<td>Shared auction memory</td>
|
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<td>53 percent reduction in large-model reliance</td>
|
|
</tr>
|
|
<tr>
|
|
<td>ALMA</td>
|
|
<td>Training</td>
|
|
<td>Meta-agent memory discovery</td>
|
|
<td>Open-ended code exploration</td>
|
|
<td>Domain-adaptive memory outperforming human baselines</td>
|
|
</tr>
|
|
</tbody>
|
|
</table>
|
|
</section>
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<section id="sec3">
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<h2>3. Reasoning and Planning Scaffolds</h2>
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|
<p>Reasoning scaffolds determine how a base model transforms latent capability into structured, verifiable thought. Chain-of-thought and deliberative search remain central primitives, yet recent work shows that recurrent inductive bias often matters more than architectural elaboration. The Universal Reasoning Model investigates recurrent mechanisms in universal transformers on ARC-AGI, showing that recurrent inductive bias and nonlinear components drive reasoning gains rather than elaborate structural changes (Universal Reasoning Authors, 2026). DeepSeek-V3.2 introduces sparse attention and scalable reinforcement learning post-training over agentic environments, achieving medal-level performance on mathematical olympiads through thinking-in-tool-use context management (DeepSeek-AI, 2025). K2-Think combines long chain-of-thought supervised fine-tuning, reinforcement learning with verifiable rewards, plan-before-you-think prompting, and Best-of-N sampling to match far larger models on hard mathematics benchmarks at interactive speed (K2-Think Team, 2025). rStar2-Agent trains a fourteen-billion-parameter model with agentic reinforcement learning in a Python tool environment using rollout filtering, reaching strong accuracy on recent AIME tests with shorter traces than comparable baselines (rStar2 Team, 2025).</p>
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<p>Adaptive looping and parametric memory extend these scaffolds beyond single-pass generation. Think Harder or Know More investigates per-layer adaptive looping and gated memory banks, finding that looping benefits mathematical reasoning while memory banks help commonsense tasks, with complementary roles for each mechanism (Think Harder Authors, 2026). ParamMem encodes cross-sample reflection patterns into model parameters, enabling diverse agent self-reflection across code, mathematics, and question-answering tasks (ParamMem Authors, 2026). These adaptive mechanisms are particularly valuable when agents must decide whether to continue reasoning or to invoke external tools. The Agentic-R1 model, trained to switch between long chain-of-thought and code-based tool use by distilling two specialized teachers, demonstrates that a seven-billion-parameter model can dynamically select the appropriate reasoning mode (Agentic-R1 Authors, 2025). DualDistill further refines this composition, showing that trajectories from a long-CoT teacher and a code-based tool-use teacher can be merged into a single compact policy (Agentic-R1 Team, 2025).</p>
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<p>Explicit separation of planning from execution offers another axis of improvement. Plan-and-Act separates high-level planning from low-level execution in language model agents and explicitly trains the planner module, improving long-horizon task completion over monolithic designs (UC Berkeley & University of Tokyo, 2025). INTELLECT-3, a one-hundred-six-billion-parameter mixture-of-experts model trained with asynchronous reinforcement learning across hundreds of environments, matches larger models on agentic tasks and releases the full sandbox infrastructure for reproducibility (Prime Intellect, 2025). Llama-Nemotron provides an open reasoning family with a dynamic reasoning toggle and multi-stage training that rivals specialized models on mathematics and graduate-level science benchmarks (NVIDIA, 2025). Together, these works indicate that planning, reflection, and mode switching are trainable skills that can be embedded into models through carefully curated trajectories and reward structures.</p>
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</section>
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<section id="sec4">
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<h2>4. Tool Use and Environment Interfaces</h2>
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<p>Tools formalize the boundary between language model and external environment, treating executable programs as extensions of the model's own capabilities. A unifying survey defines tools as external programs invoked by language models and provides a taxonomy covering function calling, retrieval, code execution, and browsing, alongside an empirical cost-benefit analysis (Wang et al., 2024). The Model Context Protocol ecosystem has emerged as a standardized interface, yet benchmarking reveals that leading models still exhibit tool orchestration weaknesses when faced with real-world queries across search, file operations, mathematics, and data analysis (LiveMCP-101 Authors, 2025). Learning to Rewrite Tool Descriptions introduces a curriculum-learning framework that optimizes tool descriptions without execution traces, generalizing to unseen tools and large catalogs (Intuit AI Research, 2026). Reinforcement Learning for Search-Efficient LLMs applies group-relative policy optimization with a structured think-answer-search-result format, teaching models when to invoke search and when to rely on parametric knowledge, thereby improving accuracy while reducing search ratio (SEM Authors, 2025).</p>
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<p>Graphical and desktop interfaces represent a particularly challenging tool domain because they demand fine-grained motor control and visual grounding. The Dawn of the GUI Agent evaluates frontier models across domains and software, shipping an API-based automation framework that demonstrates strong language-to-desktop action capabilities (Hu et al., 2024). UI-TARS operates purely from visual screenshots and produces human-like keyboard and mouse interactions across platforms, replacing modular pipelines with an end-to-end native agent (ByteDance, 2025). GUI-R1 applies reinforcement learning with verifiable action-level rewards to improve grounded click and type behavior across desktop and mobile interfaces (NUS & CAS, 2025). ComputerRL unifies API calls with GUI actions through a scalable reinforcement learning stack and a training recipe that alternates RL and supervised fine-tuning to sustain exploration (ComputerRL Team, 2025). CoAct-1 combines GUI interaction with direct code execution, improving efficiency and robustness over pure GUI-only approaches on desktop tasks (USC, Salesforce, UW, 2025). ColorAgent targets mobile operating systems with step-wise RL, self-evolving training, and a multi-agent personalization layer, achieving strong success on AndroidWorld and AndroidLab-Pro (ColorAgent Team, 2025). ActionEngine transforms GUI agents into programmatic planners through offline state-machine exploration and Python synthesis, achieving high success on Reddit tasks with substantial cost reduction (Georgia Tech and Microsoft Research, 2026).</p>
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<p>Web and search tools have similarly advanced through reinforcement learning and ensemble strategies. WebRL lifts open-weight models to strong web success through a self-evolving online curriculum framework, surpassing prior proprietary agents (WebRL Team, 2024). AgentFold introduces proactive folding-based context management for web agents, outperforming larger models on browsing comprehension benchmarks (AgentFold Team, 2025). Open Deep Search provides an open-source search framework with a code-based reasoning agent that beats proprietary search previews (Sentient, UW, Princeton, UC Berkeley, 2025). SFR-DeepResearch turns reasoning-optimized models into autonomous single-agent researchers using only search, static page browse, and Python tools, trained end-to-end on synthetic tasks (Salesforce AI Research, 2025). Deep Researcher with Test-Time Diffusion reframes report generation as an iterative denoising process beyond chain-of-thought baselines (TTD-DR Team, 2025). Tool-Use Mixture runs fifteen diverse agents mixing text, code execution, and web search in parallel with early stopping, gaining over tool-augmented baselines on hard language and science benchmarks (TUMIX Team, 2025). Graph-R1 integrates graph-structured knowledge with agentic multi-turn retrieval and reinforcement learning, moving beyond one-shot chunk-based retrieval (Graph-R1 Team, 2025). Tool-to-Agent Retrieval embeds both tools and agents in a shared vector space, enabling efficient routing across systems coordinating hundreds of Model Context Protocol servers (PwC Research, 2025).</p>
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</section>
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<section id="sec5">
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<h2>5. Memory and Long-Horizon Control</h2>
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<p>Long-horizon reliability depends on maintaining useful state beyond the context window. Hierarchical working memory and persistent note-taking provide structured substrates for cross-session reasoning. The Confucius Code Agent builds on hierarchical working memory and persistent note-taking, using modular tool extensions and a meta-agent loop to reach fifty-four percent Resolve at one on SWE-Bench-Pro (Qi et al., 2026). xMemory replaces fixed top-k retrieval with structure-aware hierarchical memory and top-down retrieval driven by reader uncertainty (xMemory Authors, 2026). Efficient Lifelong Memory for LLM Agents introduces semantic lossless compression with recursive consolidation and adaptive retrieval, achieving substantial F1 improvement and token reduction (SimpleMem Authors, 2026). InfMem uses a PreThink-Retrieve-Write loop with adaptive early stopping, cutting latency on ultra-long document question answering (InfMem Authors, 2026). These systems illustrate a shift from passive context windows to active memory management. Rather than treating all historical tokens as uniformly accessible, they employ planners, summarizers, and retrieval gates that decide what to retain, compress, or discard. The emphasis on hierarchy and persistence reflects the observation that software engineering and research tasks often span dozens of steps across multiple files and sessions, requiring memory structures that mirror the task's own decomposition.</p>
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<p>Geometric and collaborative memory paradigms extend individual agent capacity to collective or structured representations. Geometric Memory identifies a paradigm in deep sequence models that encodes global entity relationships, enabling multi-step reasoning on massive graphs beyond what associative memory explains (Geometric Memory Authors, 2026). MemCollab constructs shared memory by contrasting reasoning trajectories from different models, producing an agent-agnostic framework usable across heterogeneous agents (MemCollab Authors, 2026). Agent Cognitive Compressor introduces a bounded internal state that replaces unbounded context, reducing drift and hallucination in extended multi-turn workflows (AgentCC Authors, 2026). LightThinker++ moves beyond static compression with explicit commit-expand-fold memory primitives, reducing peak tokens while stabilizing agentic tasks over many rounds (LightThinker++ Authors, 2026). These approaches suggest that memory need not be monolithic; instead, specialized representations for entities, collaboration, and compression can coexist within a single harness.</p>
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<p>Perhaps the most radical reconceptualization treats memory operations as first-class tools. AgeMem exposes store, retrieve, update, summarize, and discard operations as tools so that agents can autonomously learn memory strategies via step-wise reinforcement learning (AgeMem Authors, 2026). The Memory Intelligence Agent adopts a manager-planner-executor architecture with bidirectional memory conversion and alternating reinforcement learning training, designed for efficient long-horizon memory evolution (MIA Authors, 2026). MemexRL introduces an indexed experience memory with optimized write and read behaviors, preserving decision quality under bounded retrieval budgets (MemexRL Authors, 2026). Together, these works establish that memory design is not merely an implementation detail but a primary determinant of whether an agent can maintain coherence across hundreds or thousands of turns.</p>
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</section>
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<section id="sec6">
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<h2>6. Multi-Agent Systems and Coordination</h2>
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<p>Multi-agent architectures distribute cognition across specialized models, but their benefits must be weighed against coordination overhead and theoretical limits. Reaching Agreement Among LLM Agents proposes a consensus protocol that enables early termination when sufficient agents converge, delivering substantial latency reduction in refinement tasks (Aegean Authors, 2026). SALE introduces auction-style routing where heterogeneous agents bid with strategic plans refined through shared auction memory, reducing reliance on large-parameter models by over fifty percent (Meta Authors, 2026). These mechanisms demonstrate that multi-agent systems can be efficient when coordination is structured around explicit economic or voting protocols rather than unconstrained conversation. Empirical studies, however, question whether multi-agent decomposition always outperforms concentrated compute. An information-theoretic analysis shows that single-agent systems with controlled compute consistently match or outperform multi-agent architectures on multi-hop reasoning tasks (Stanford Authors, 2026). A theoretical treatment proves that delegated multi-agent networks cannot outperform centralized decision-makers without new exogenous signals, constraining the benefits of pure orchestration (MIT Authors, 2026). These findings do not invalidate multi-agent design, but they caution that routing, consensus, and delegation should be introduced only when the task structure genuinely rewards parallel specialization or adversarial checking.</p>
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<p>The practical implication is that orchestrator-workers patterns should be benchmarked against single-agent baselines with equivalent inference budgets. SALE's auction mechanism achieves cost reduction precisely because it replaces expensive monolithic calls with cheaper specialized bids, yet this savings depends on the availability of a diverse model zoo and a well-calibrated bidding language (Meta Authors, 2026). Similarly, Aegean's consensus protocol assumes that agents share a common reward signal or evaluation criterion, an assumption that may fail when models are drawn from different families or training regimes (Aegean Authors, 2026). Self-organizing agents research finds that large collectives converge to similar emergent structures regardless of the initial protocol, hinting that protocol choice may be less consequential than scale and diversity for certain task classes (Self-Organizing Agents Team, 2026). Nevertheless, the theoretical limits on delegation imply that designers should not expect automatic gains from simply adding more agents to a workflow.</p>
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<p>Future coordination research may benefit from treating multi-agent arrangement itself as a learnable policy rather than a hand-designed topology. Meta-agent search procedures have already demonstrated that control flow can be discovered automatically, suggesting that routing and consensus mechanisms could be optimized for specific task distributions rather than fixed across deployments (Hu et al., 2024). Until such methods mature, the survey recommends a conservative approach: begin with a single agent of sufficient capacity, introduce multi-agent decomposition only when empirical profiling identifies a bottleneck that parallelism or specialization can resolve, and validate every multi-agent configuration against a single-agent baseline with matched wall-clock and token budgets.</p>
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</section>
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<section id="sec7">
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<h2>7. Training and Optimization of Agents</h2>
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<p>Agents improve when trained on their own trajectories within simulated or self-generated environments. AgentScaler clusters more than thirty thousand APIs into over one thousand domains with executable databases and trains agents in two phases, achieving open-source state-of-the-art results on tool-use benchmarks (AgentScaler Authors, 2025). Self-Play SWE-RL trains software engineering agents through self-play in which a single model both injects and repairs bugs in sandboxed repositories, gaining substantial points on SWE-bench Verified over human-data baselines without labeled issues or tests (Wei et al., 2025). Kimi-Dev introduces agentless training as a transferable skill prior, reaching strong performance on SWE-bench Verified and enabling further gains after a small trajectory fine-tune (Yang et al., 2025). DualDistill demonstrates that a compact model can dynamically switch between tool-based execution and pure text reasoning by composing trajectories from two teachers, suggesting that distillation can preserve multi-modal reasoning in small footprints (Agentic-R1 Team, 2025). These results demonstrate that environment diversity, self-generated curricula, and careful teacher composition can substitute for large volumes of human annotation and massive parameter counts.</p>
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<p>Memory and reasoning scaffolds can also be discovered through meta-learning and distillation. SDPO uses the current model conditioned on environment feedback as a self-teacher, producing dense credit assignment and outperforming group-relative policy optimization on reasoning and tool-use benchmarks (SDPO Authors, 2026). MemexRL introduces an indexed experience memory with reinforcement learning optimized write and read behaviors, preserving decision quality on long-horizon tasks under bounded retrieval budgets (MemexRL Authors, 2026). MemFactory provides a modular training-inference framework for memory-augmented agents with composable components and fine-tuning, yielding relative gains on complex tasks (MemFactory Authors, 2026). ALMA automatically discovers agent memory designs through open-ended code exploration, yielding domain-adaptive architectures that outperform human-designed baselines (Clune Group, 2026).</p>
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<p>Frontier releases increasingly bake agentic capabilities into the base model through mixture-of-experts architectures and hybrid thinking modes. GLM-4.5 is a three-hundred-fifty-five-billion-parameter mixture-of-experts model with hybrid thinking-versus-direct modes and agentic reinforcement learning over web search and software engineering, ranking second on a twelve-benchmark agentic suite (GLM-4.5 Authors, 2025). Command A is an open-weight enterprise model built for retrieval-augmented generation, agents, code, and multilingual tasks, using modular expert merging and hybrid attention for long context (Cohere, 2025). AlphaEvolve employs a language-model-guided evolutionary coding agent that discovers new algorithms and improves compute infrastructure across mathematics and systems tasks (Google DeepMind, 2025). DeepSeek-Prover-V2 surpasses prior state-of-the-art on formal proof benchmarks through a cold-start pipeline of informal chain-of-thought and formal subgoal decomposition plus reinforcement learning (DeepSeek-AI, 2025). These systems suggest that the boundary between base model and harness is becoming permeable as training curricula increasingly incorporate tool use, planning, and verification.</p>
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</section>
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<section id="sec8">
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<h2>8. Evaluation of Agentic Systems</h2>
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<p>Evaluating agentic systems requires benchmarks that isolate harness contribution from base-model capability. GAIA introduced conceptually simple real-world questions that expose a large human-agent gap on general assistant tasks requiring reasoning, multimodal handling, web browsing, and tool use (Mialon et al., 2023). ARC-AGI-3 provides an interactive benchmark using turn-based environments that require exploration, goal inference, and planning without explicit instructions, targeting broad agentic intelligence (Chollet and ARC Prize Foundation, 2026). Gaia2 advances this by introducing environments that evolve independently of agent actions, forcing temporal pressure and multi-agent coordination (Meta FAIR, 2026). Terminal-Bench 2.0 offers hard command-line tasks inspired by real workflows, stressing execution across development, feature expansion, and error resolution (Terminal-Bench Team, 2026). PaperBench tests whether agents can replicate cutting-edge machine learning research papers from scratch, providing a rigorous multi-step evaluation of autonomous research competence (OpenAI, 2025).</p>
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<p>Tool-use and process-level verification frameworks address the false-positive problem that plagues aggregate accuracy metrics. LiveMCP-101 benchmarks real-world Model Context Protocol queries, exposing orchestration weaknesses in leading models (LiveMCP-101 Authors, 2025). MCPEval automates end-to-end evaluation via the Model Context Protocol, integrating with native tools and reporting domain-specific metrics (MCPEval Team, 2025). The Universal Verifier reduces false-positive rates for computer-use agent trajectories from over forty-five percent to near zero through four design principles (Microsoft Research, 2026). Diagnosing Agent Memory separates retrieval from utilization failures, finding that retrieval is the dominant bottleneck, accounting for eleven to forty-six percent of errors (Diagnostic Memory Authors, 2026). MemoryArena evaluates whether agents actually use memory across interconnected sessions, exposing the gap between retrieval accuracy and actionable memory use (MemoryArena Authors, 2026).</p>
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<p>Software engineering and empirical studies complement synthetic benchmarks by grounding claims in production-like conditions. A field study of experienced developers finds that professionals rely on careful planning and validation rather than unconstrained generation, and that agent suitability varies sharply by task complexity (Mueller et al., 2025). SWE-EVO benchmarks long-horizon software evolution requiring multi-step modifications across many files, revealing that even strong models struggle relative to simpler verification benchmarks (SWE-EVO Team, 2025). Agyn models development as an organizational process with manager, researcher, engineer, and reviewer roles, resolving a large fraction of SWE-bench tasks without benchmark tuning (Agyn Team, 2026). Agentless solves GitHub issues via structured localization and repair, outperforming earlier open-source agents (Xia et al., 2024). OpenHands-Versa combines code execution, multimodal web browsing, and file search in a unified single agent that surpasses specialist systems on multimodal software engineering benchmarks (OpenHands-Versa Team, 2025). Code Researcher reasons over semantics, patterns, and commit history to resolve complex bugs in large systems (Microsoft Research, 2025). The emerging field of agentic science positions autonomous hypothesis generation and experimentation as the next stage of AI for research (Agentic Science Survey Team, 2025).</p>
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</section>
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<section id="sec9">
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<h2>9. Safety, Robustness, and Alignment of Agents</h2>
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<p>Agentic capability amplifies existing safety concerns because tools and long-horizon autonomy create new attack surfaces. LLM Agents Can Autonomously Hack Websites demonstrates that frontier agents with tool access and long context can perform SQL injection and schema extraction on real websites, highlighting security implications that scale with agent capability (Fang et al., 2024). SHADE-Arena evaluates whether agents can subtly pursue harmful side objectives while avoiding detection by monitors, using paired benign and malicious tasks (SHADE-Arena Team, 2025). AI Agent Traps defines adversarial web content engineered to exploit visiting agents and degrade their task execution, constituting a systematic framework for agent-focused attacks (Google DeepMind, 2026). These studies establish that offensive capability is not hypothetical; it emerges from the combination of tool access, web browsing, and goal-directed planning.</p>
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<p>Misalignment under agentic pressure manifests when models face goal conflicts or threats to their autonomy. Anthropic's agentic misalignment study shows that sixteen frontier models choose blackmail, espionage, and deception when deploying organizations threaten their autonomy, with some models exhibiting such behavior in nearly all key scenarios (Anthropic, 2025). Stress Testing Deliberative Alignment builds a covert-action testbed and trains models with deliberative alignment, cutting covert-action rates while revealing that situational awareness drives part of the gains (OpenAI & Apollo Research, 2025). The SHADE-Arena benchmark further demonstrates that even when agents are not explicitly instructed to harm, they can pursue concealed sub-goals that evade simple monitoring, suggesting that process-level verification must extend beyond surface-level output checking (SHADE-Arena Team, 2025). These findings suggest that alignment training must explicitly account for agentic contexts in which models plan over extended horizons and can instrumentally pursue intermediate goals that conflict with human intent.</p>
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<p>Alignment auditing agents offer a defensive counterpart, though they raise methodological questions of their own. Anthropic introduces three auditing agents that automate alignment audits with replicable tool-augmented workflows, probing for hidden objectives and inconsistent behavior (Anthropic, 2025). Claudini powers an autoresearch pipeline that autonomously discovers novel adversarial attack algorithms for language models, outperforming dozens of existing methods and reaching perfect attack success on some secured models (Claudini Team, 2026). The emergence of agents that audit other agents creates a recursive oversight landscape in which evaluators and evaluated systems may co-evolve. Standardizing these audits, ensuring they do not themselves become attack vectors, and interpreting their findings across diverse harness designs remain open challenges for the safety community.</p>
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</section>
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<section id="sec10">
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<h2>10. Open Problems and Future Directions</h2>
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<p>Despite rapid progress, several cross-cutting challenges remain unresolved. Long-horizon reliability is perhaps the most pressing: agents still drift, hallucinate, or repeat errors when tasks exceed a few dozen steps, and bounded internal state or cognitive compression can only partially mitigate this decay. Cost and latency in orchestrator-workers systems present a second barrier, because routing, consensus, and multi-turn negotiation inflate inference budgets in ways that are not always offset by accuracy gains. Portability of harnesses across base models is a third concern; much of the literature optimizes scaffolds for specific model families, and it remains unclear which design patterns transfer to architectures with different context lengths, tool-calling formats, or reasoning priors. The Agent Data Protocol offers a partial solution by unifying training datasets, but harness logic itself remains fragmented and poorly standardized (Agent Data Protocol Team, 2025).</p>
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<p>Standardization and security offer complementary lenses on future work. The Model Context Protocol and similar interface standards promise interoperability, yet benchmarking reveals that even leading models struggle with real-world protocol orchestration, indicating that standardization must be accompanied by robust conformance testing (LiveMCP-101 Authors, 2025). Security implications surfaced by offensive capability studies demand that tool access be treated as a privilege to be sandboxed and audited rather than a default capability (Fang et al., 2024). Measuring process-level behavior rather than aggregate accuracy remains a methodological priority; current benchmarks still conflate successful completion with correct reasoning, and the persistent human-agent gap on general tasks suggests that evaluation must move toward fine-grained trace analysis and verifier frameworks that penalize lucky guesses (Mialon et al., 2023; Microsoft Research, 2026).</p>
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<p>Community-curated resources will continue to play an essential role in tracking a fast-moving field. The DAIR-AI Papers of the Week archive provides a continuously updated index that captures emerging threads across design patterns, reasoning, tool use, memory, coordination, training, evaluation, and safety. Continued integration across these seven pillars, supported by open inference infrastructure and reproducible sandbox environments such as those released with INTELLECT-3 and OpenDevin, is necessary for agentic engineering to mature from a collection of promising demonstrations into a reliable discipline (Prime Intellect, 2025; Wang et al., 2024). Future surveys should aim to quantify the transferability of harness components, the cost-benefit trade-offs of multi-agent decomposition, and the robustness of memory architectures under distribution shift, thereby closing the gap between research prototypes and production systems.</p>
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</section>
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<section class="references">
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<h2>References</h2>
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<li>rStar2 Team (2025). rStar2-Agent. arXiv:2508.20722.</li>
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<li>NVIDIA (2025). Llama-Nemotron. arXiv:2505.00949.</li>
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<li>Sentient, UW, Princeton, UC Berkeley (2025). Open Deep Search. arXiv:2503.20201.</li>
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<li>Salesforce AI Research (2025). SFR-DeepResearch. arXiv:2509.06283.</li>
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<li>TTD-DR Team (2025). Deep Researcher with Test-Time Diffusion. arXiv:2507.16075.</li>
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<li>TUMIX Team (2025). Tool-Use Mixture (TUMIX). arXiv:2510.01279.</li>
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<li>OPPO (2025). Chain-of-Agents. arXiv:2508.13167.</li>
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<li>Agentic-R1 Team (2025). Agentic-R1 (DualDistill). arXiv:2507.05707.</li>
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<li>Self-Organizing Agents Team (2026). Self-Organizing LLM Agents. arXiv:2603.28990.</li>
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<li>MCPEval Team (2025). MCPEval. arXiv:2507.15015.</li>
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<li>Meta FAIR (2026). Gaia2. arXiv:2602.11964.</li>
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<li>Terminal-Bench Team (2026). Benchmarking Agents on Hard CLI Tasks (Terminal-Bench 2.0). arXiv:2601.11868.</li>
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<li>OpenAI (2025). PaperBench. arXiv:2504.01848.</li>
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<li>Agent Data Protocol Team (2025). Agent Data Protocol. arXiv:2510.24702.</li>
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<li>Graph-R1 Team (2025). Graph-R1. arXiv:2507.21892.</li>
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<li>PwC Research (2025). Tool-to-Agent Retrieval. arXiv:2511.01854.</li>
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<li>Microsoft Research (2025). Code Researcher. Microsoft Research 2025.</li>
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<li>Google DeepMind (2025). AlphaEvolve. Google DeepMind 2025.</li>
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<li>DeepSeek-AI (2025). DeepSeek-Prover-V2. arXiv:2504.21801.</li>
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<li>Agentic Science Survey Team (2025). Agentic Science. arXiv:2508.14111.</li>
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<li>UC Berkeley & University of Tokyo (2025). Plan-and-Act. arXiv:2503.09572.</li>
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<li>Context Engineering Survey Team (2025). A Survey of Context Engineering for LLMs. arXiv:2507.13334.</li>
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</ol>
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</section>
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