{"paper":{"title":"The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey","license":"http://creativecommons.org/licenses/by/4.0/","headline":"AI agent architectures achieve complex goals through specific choices in leadership, communication styles, and planning-execution-reflection phases.","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Alex Chao, Mason Sawtell, Sandi Besen, Tula Masterman","submitted_at":"2024-04-17T17:32:41Z","abstract_excerpt":"This survey paper examines the recent advancements in AI agent implementations, with a focus on their ability to achieve complex goals that require enhanced reasoning, planning, and tool execution capabilities. The primary objectives of this work are to a) communicate the current capabilities and limitations of existing AI agent implementations, b) share insights gained from our observations of these systems in action, and c) suggest important considerations for future developments in AI agent design. We achieve this by providing overviews of single-agent and multi-agent architectures, identif"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Our contribution outlines key themes when selecting an agentic architecture, the impact of leadership on agent systems, agent communication styles, and key phases for planning, execution, and reflection that enable robust AI agent systems.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the selected AI agent implementations are representative of the broader landscape and that the authors' observations of their capabilities and limitations are comprehensive and unbiased.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A survey of emerging AI agent architectures that organizes single and multi-agent designs around reasoning, planning, tool use, communication, and reflection phases.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"AI agent architectures achieve complex goals through specific choices in leadership, communication styles, and planning-execution-reflection phases.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"2373a5ffb2db64513e88a4d6e8f33dfa05cbddb72bc39ef0a45ed9c5d24e1ff8"},"source":{"id":"2404.11584","kind":"arxiv","version":1},"verdict":{"id":"57b1545a-174d-4f48-807f-b0067496b04f","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-16T23:14:12.413730Z","strongest_claim":"Our contribution outlines key themes when selecting an agentic architecture, the impact of leadership on agent systems, agent communication styles, and key phases for planning, execution, and reflection that enable robust AI agent systems.","one_line_summary":"A survey of emerging AI agent architectures that organizes single and multi-agent designs around reasoning, planning, tool use, communication, and reflection phases.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the selected AI agent implementations are representative of the broader landscape and that the authors' observations of their capabilities and limitations are comprehensive and unbiased.","pith_extraction_headline":"AI agent architectures achieve complex goals through specific choices in leadership, communication styles, and planning-execution-reflection phases."},"references":{"count":38,"sample":[{"doi":"","year":2024,"title":"AutoGPT+P: Affordance-based Task Planning with Large Language Models","work_id":"8ecd1ca0-0b07-43ff-be44-fb46e5417b8e","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors","work_id":"3187ed22-d803-437a-88c1-abab6eeb2af6","ref_index":2,"cited_arxiv_id":"2308.10848","is_internal_anchor":true},{"doi":"","year":2021,"title":"Training Verifiers to Solve Math Word Problems","work_id":"acab1aa8-b4d6-40e0-a3ee-25341701dca2","ref_index":3,"cited_arxiv_id":"2110.14168","is_internal_anchor":true},{"doi":"","year":2024,"title":"Large Language Model-based Human-Agent Collaboration for Complex Task Solving","work_id":"d5b57b44-cef5-4221-ae91-95bbb34c382a","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Bias and fairness in large language models: A survey","work_id":"d11a418c-9536-4273-a406-81796c943c0f","ref_index":6,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":38,"snapshot_sha256":"0a4f111e7e86a1b0a0d0116d1c85f00e53773b5a85a092cdaa784460637e5dbd","internal_anchors":13},"formal_canon":{"evidence_count":1,"snapshot_sha256":"6448db650fa04edb0fec04a16aa3ed4250140a3c22d798df30a0d01a2a0e75a7"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}