--- schema: formation.pattern/v0.1 kind: pattern visibility: public aliases: - 'hierarchical agents' - 'hierarchical multi-agent system' - 'recursive delegation' - 'hierarchical teams (LangGraph)' canonical_url: https://topologyindex.com/patterns/hierarchical_delegation.md description: 'Work is delegated down several levels: assignees become assigners.' executable_here: false executor_requirements: - agent_to_agent_messages - role_creation_at_runtime family: hierarchy findings_page: /patterns/hierarchical_delegation/findings.md observed_in: - /research/openai-hugging-face-agent-coordination.md path: /patterns/hierarchical_delegation.md pattern: hierarchical_delegation pattern_index: /patterns/index.md product_api_version: v1 published_findings: - benchmarks: - HumanEval - MBPP - SoftwareDev compared_against: 'Chat-based multi-agent frameworks such as ChatDev and AgentVerse, and single models' direction: helped source_id: arxiv:2308.00352 task_domain: 'Software engineering and code generation' task_domains: - coding url: https://arxiv.org/abs/2308.00352 - benchmarks: - HumanEval - CIAR - CommonMT - FairEval compared_against: 'Linear and flat multi-agent structures' direction: helped source_id: arxiv:2408.00989 task_domain: 'Code generation, math, translation and text evaluation' task_domains: - coding - reasoning - evaluation url: https://arxiv.org/abs/2408.00989 - benchmarks: - MMLU - WikiQA - Camera compared_against: 'Strong reasoning models and multi-agent frameworks such as AgentVerse' direction: helped source_id: arxiv:2502.11098 task_domain: 'Question answering and advertisement text generation' task_domains: - reasoning url: https://arxiv.org/abs/2502.11098 - benchmarks: [] compared_against: 'A single centralized decision-maker with equivalent information access' direction: hurt source_id: arxiv:2603.26993 task_domain: 'Multi-stage planning and decision making' task_domains: - reasoning url: https://arxiv.org/abs/2603.26993 - benchmarks: - MAST-Data compared_against: 'The unmodified ChatDev configuration' direction: no_clear_gain source_id: arxiv:2503.13657 task_domain: 'Software development tasks' task_domains: - coding url: https://arxiv.org/abs/2503.13657 - benchmarks: [] compared_against: 'A single agent with the same model and no vulnerability description, and ablations without the task-specific agents or the hierarchy' direction: helped source_id: arxiv:2406.01637 task_domain: 'Offensive security: exploiting unknown real-world web vulnerabilities' task_domains: - security url: https://arxiv.org/abs/2406.01637 - benchmarks: - CVE-Bench compared_against: 'The Cybench single agent and AutoGPT under the same model and iteration budget' direction: mixed source_id: arxiv:2503.17332 task_domain: 'Offensive security: exploiting real-world web application vulnerabilities' task_domains: - security url: https://arxiv.org/abs/2503.17332 qualifying_evidence: [] references: - https://arxiv.org/abs/2308.00352 schema_version: v0.1 title: 'When to use the hierarchical_delegation multi-agent pattern (hierarchical agents, hierarchical multi-agent system)?' unmet_executor_requirements: - agent_to_agent_messages - role_creation_at_runtime --- # When to use the hierarchical_delegation multi-agent pattern (hierarchical agents, hierarchical multi-agent system)? **Short answer:** test it only when work decomposes recursively and each level can state an acceptance condition for the level below it; otherwise start from the [decision guide](/patterns/index.md) row for your task. A hypothesis, not a ranking; [published findings](/patterns/hierarchical_delegation/findings.md) keep the unfavourable ones. The `hierarchical_delegation` multi-agent pattern (family `hierarchy`): work is delegated down several levels: assignees become assigners. Also called: hierarchical agents, hierarchical multi-agent system, recursive delegation, hierarchical teams (LangGraph). ## The arrangement - Structure: A tree of roles that deepens as the work is broken down. - Control: Each level assigns to the level below and reports to the level above. - Shared state: Per-branch context; siblings share nothing. ## When it is hypothesised to fit Hypothesised to fit when work decomposes recursively and each level can state an acceptance condition for the level below it. A hypothesis to test on a workload, not a finding: this service has not tested it. ## Failure modes to watch for - Instructions lose fidelity at every level, and so does what comes back. - Depth multiplies cost in a way that is easy to miss while authoring. - A failure deep in the tree reaches the top as a summary of a summary. These are things to watch for, not outcomes anyone measured here. ## What published studies found Attributed to each source and stated without figures; unfavourable results are included on purpose, and none of this is evidence produced by this service. What each was compared with is in `published_findings`; each finding in words, with its caveat, is at [/patterns/hierarchical_delegation/findings.md](/patterns/hierarchical_delegation/findings.md). Reviewed 2026-09-23. Each label says how this pattern fared against what it was compared with, as the source reports it; the labels are defined at [/docs/schemas/pattern/v0.1.md](/docs/schemas/pattern/v0.1.md). - **Helped**: [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://arxiv.org/abs/2308.00352) - **Helped**: [On the Resilience of LLM-Based Multi-Agent Collaboration with Faulty Agents](https://arxiv.org/abs/2408.00989) - **Helped**: [Talk Structurally, Act Hierarchically: A Collaborative Framework for LLM Multi-Agent Systems](https://arxiv.org/abs/2502.11098) - **Hurt**: [On the Reliability Limits of LLM-Based Multi-Agent Planning](https://arxiv.org/abs/2603.26993) - **No clear gain**: [Why Do Multi-Agent LLM Systems Fail?](https://arxiv.org/abs/2503.13657) - **Helped**: [Teams of LLM Agents can Exploit Zero-Day Vulnerabilities](https://arxiv.org/abs/2406.01637) - **Mixed**: [CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilities](https://arxiv.org/abs/2503.17332) ## Sources that describe it Where the arrangement is described. A source is a reason to test it, never a result for it. - [MetaGPT: Meta Programming for a Multi-Agent Collaborative Framework (Hong et al.)](https://arxiv.org/abs/2308.00352) ## Can this deployment execute it No. The runner does not provide everything this arrangement needs. What is missing: - `agent_to_agent_messages` (a channel on which one agent addresses another): The adapter sees a single role invocation, and the tool surface offers no channel to another agent. - `role_creation_at_runtime` (roles that do not exist until the work asks for them): Roles come from the registered formation; the supervisor can only move between declared roles. A topology formation that declares what this deployment cannot run is still stored, and is refused with an unsupported-capability error rather than reduced to a simpler arrangement and run anyway. ## Evidence for this pattern None. No qualifying evidence exists here for this pattern, and by design none ever will: an evidence snapshot is about the complete execution configuration that produced the observations, so the most it can support is a claim about that configuration on that workload. ## Observed in Reports that describe this arrangement. A report is a reason to test an arrangement, never a result for it. - [Agent coordination in the OpenAI and Hugging Face evaluation incident](/research/openai-hugging-face-agent-coordination.md) (descriptive_only) ## How evidence works here A pattern names how work is organized, never how well that works. Evidence in this service attaches to a complete execution configuration — a topology formation together with an execution lock pinning models, tools and runtime — and never to a pattern on its own, so no page here carries a success rate, a cost or a run count for an arrangement.