{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:P5APWQ5HAK3XBQZI33GIIVLQMR","short_pith_number":"pith:P5APWQ5H","schema_version":"1.0","canonical_sha256":"7f40fb43a702b770c328decc8455706471ceaebcdadb63856ed122ac2777285e","source":{"kind":"arxiv","id":"2404.11584","version":1},"attestation_state":"computed","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"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":true,"formal_links_present":true},"canonical_record":{"source":{"id":"2404.11584","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-04-17T17:32:41Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"19cde5be34bac8f68ced6e1aaea2c1467c2e1d0ae23ef47d7109b13640299d08","abstract_canon_sha256":"b3adce2901d010c1a7cd6011ee70fbbec4e008636e305ebf3550989bc8e6df35"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:38:46.268431Z","signature_b64":"r0UUnZx8XArWkcaLpbExnC0uCP97eHsehtTMsEbd5QffGbScLsBnXGHvXLW8M4NZgMP6xQFzITorkBZgMAr1Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7f40fb43a702b770c328decc8455706471ceaebcdadb63856ed122ac2777285e","last_reissued_at":"2026-05-17T23:38:46.267988Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:38:46.267988Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2404.11584","created_at":"2026-05-17T23:38:46.268059+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.11584v1","created_at":"2026-05-17T23:38:46.268059+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.11584","created_at":"2026-05-17T23:38:46.268059+00:00"},{"alias_kind":"pith_short_12","alias_value":"P5APWQ5HAK3X","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_16","alias_value":"P5APWQ5HAK3XBQZI","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_8","alias_value":"P5APWQ5H","created_at":"2026-05-18T12:33:37.589309+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":33,"internal_anchor_count":33,"sample":[{"citing_arxiv_id":"2503.21460","citing_title":"Large Language Model Agent: A Survey on Methodology, Applications and Challenges","ref_index":23,"is_internal_anchor":true},{"citing_arxiv_id":"2504.01990","citing_title":"Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems","ref_index":65,"is_internal_anchor":true},{"citing_arxiv_id":"2505.16120","citing_title":"LLM-Powered AI Agent Systems and Their Applications in Industry","ref_index":3,"is_internal_anchor":true},{"citing_arxiv_id":"2605.17675","citing_title":"Bridging the Gap on AI-Assisted Scientific Software Development Through Transparency and Traceability","ref_index":3,"is_internal_anchor":true},{"citing_arxiv_id":"2605.16689","citing_title":"Against the Monolithic Wireless World Model: Why NextG Needs Composable and Agentic Intelligence","ref_index":49,"is_internal_anchor":true},{"citing_arxiv_id":"2604.27859","citing_title":"Rethinking Agentic Reinforcement Learning In Large Language Models","ref_index":59,"is_internal_anchor":true},{"citing_arxiv_id":"2505.23723","citing_title":"ML-Agent: Reinforcing LLM Agents for Autonomous Machine Learning Engineering","ref_index":1,"is_internal_anchor":true},{"citing_arxiv_id":"2411.18279","citing_title":"Large Language Model-Brained GUI Agents: A Survey","ref_index":202,"is_internal_anchor":true},{"citing_arxiv_id":"2508.08127","citing_title":"BlindGuard: Safeguarding LLM-based Multi-Agent Systems under Unknown Attacks","ref_index":13,"is_internal_anchor":true},{"citing_arxiv_id":"2509.02547","citing_title":"The Landscape of Agentic Reinforcement Learning for LLMs: A Survey","ref_index":23,"is_internal_anchor":true},{"citing_arxiv_id":"2509.12626","citing_title":"DoubleAgents: Human-Agent Alignment in a Socially Embedded Workflow","ref_index":23,"is_internal_anchor":true},{"citing_arxiv_id":"2509.19185","citing_title":"An Empirical Study of Testing Practices in Open Source AI Agent Frameworks and Agentic Applications","ref_index":11,"is_internal_anchor":true},{"citing_arxiv_id":"2409.02977","citing_title":"Large Language Model-Based Agents for Software Engineering: A Survey","ref_index":234,"is_internal_anchor":true},{"citing_arxiv_id":"2411.04468","citing_title":"Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks","ref_index":27,"is_internal_anchor":true},{"citing_arxiv_id":"2506.02153","citing_title":"Small Language Models are the Future of Agentic AI","ref_index":55,"is_internal_anchor":true},{"citing_arxiv_id":"2506.11763","citing_title":"DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents","ref_index":15,"is_internal_anchor":true},{"citing_arxiv_id":"2602.11224","citing_title":"Agent-Diff: Benchmarking LLM Agents on Enterprise API Tasks via Code Execution with State-Diff-Based Evaluation","ref_index":19,"is_internal_anchor":true},{"citing_arxiv_id":"2605.13848","citing_title":"GraphBit: A Graph-based Agentic Framework for Non-Linear Agent Orchestration","ref_index":36,"is_internal_anchor":true},{"citing_arxiv_id":"2603.28166","citing_title":"Evaluating Privilege Usage of Agents with Real-World Tools","ref_index":15,"is_internal_anchor":true},{"citing_arxiv_id":"2604.03976","citing_title":"Quantifying Trust: Financial Risk Management for Trustworthy AI Agents","ref_index":30,"is_internal_anchor":true},{"citing_arxiv_id":"2605.07069","citing_title":"Social Theory Should Be a Structural Prior for Agentic AI: A Formal Framework for Multi-Agent Social Systems","ref_index":66,"is_internal_anchor":true},{"citing_arxiv_id":"2605.11706","citing_title":"GRAFT: Graph-Tokenized LLMs for Tool Planning","ref_index":18,"is_internal_anchor":true},{"citing_arxiv_id":"2604.27859","citing_title":"Rethinking Agentic Reinforcement Learning In Large Language Models","ref_index":59,"is_internal_anchor":true},{"citing_arxiv_id":"2605.07069","citing_title":"Social Theory Should Be a Structural Prior for Agentic AI: A Formal Framework for Multi-Agent Social Systems","ref_index":66,"is_internal_anchor":true},{"citing_arxiv_id":"2604.27859","citing_title":"Rethinking Agentic Reinforcement Learning In Large Language Models","ref_index":59,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":1,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/P5APWQ5HAK3XBQZI33GIIVLQMR","json":"https://pith.science/pith/P5APWQ5HAK3XBQZI33GIIVLQMR.json","graph_json":"https://pith.science/api/pith-number/P5APWQ5HAK3XBQZI33GIIVLQMR/graph.json","events_json":"https://pith.science/api/pith-number/P5APWQ5HAK3XBQZI33GIIVLQMR/events.json","paper":"https://pith.science/paper/P5APWQ5H"},"agent_actions":{"view_html":"https://pith.science/pith/P5APWQ5HAK3XBQZI33GIIVLQMR","download_json":"https://pith.science/pith/P5APWQ5HAK3XBQZI33GIIVLQMR.json","view_paper":"https://pith.science/paper/P5APWQ5H","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.11584&json=true","fetch_graph":"https://pith.science/api/pith-number/P5APWQ5HAK3XBQZI33GIIVLQMR/graph.json","fetch_events":"https://pith.science/api/pith-number/P5APWQ5HAK3XBQZI33GIIVLQMR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/P5APWQ5HAK3XBQZI33GIIVLQMR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/P5APWQ5HAK3XBQZI33GIIVLQMR/action/storage_attestation","attest_author":"https://pith.science/pith/P5APWQ5HAK3XBQZI33GIIVLQMR/action/author_attestation","sign_citation":"https://pith.science/pith/P5APWQ5HAK3XBQZI33GIIVLQMR/action/citation_signature","submit_replication":"https://pith.science/pith/P5APWQ5HAK3XBQZI33GIIVLQMR/action/replication_record"}},"created_at":"2026-05-17T23:38:46.268059+00:00","updated_at":"2026-05-17T23:38:46.268059+00:00"}