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Agentic AI
Planning & Control Flow
Rumination Agent
Run a single agent through a protracted think-search-verify-revise-act loop spanning hundreds of tool calls, autonomously re-formulating hypotheses across the run.
Intent & Description
🎯 Intent
Run a single agent through a protracted think-search-verify-revise-act loop spanning hundreds of tool calls, autonomously re-formulating hypotheses across the run.
📋 Context
A team runs an agent on open-ended research and deep-investigation work — assessing whether a paper’s claims replicate, tracing the root cause of a system anomaly, scoping a novel question — where the answer cannot be reached by a short reason-act-observe loop or by a one-shot plan. The agent has retrieval, browsing, and code-execution tools and is expected to spend minutes to hours on a single question.
💡 Solution
- Each outer iteration runs five named phases: (1) think — emit an updated working hypothesis; (2) search — issue retrieval, browsing, or tool calls scoped to that hypothesis; (3) verify — check new evidence against the hypothesis with explicit pass/fail notes; (4) revise — narrow, broaden, or replace the hypothesis based on verification; (5) act — write findings, update an externalised plan, or commit an artefact. - Context is compacted between cycles by replacing prior search dumps with verified-evidence summaries, keeping the trace linear in cycles, not in tool calls. - The loop terminates on confidence threshold, budget exhaustion, or explicit answer-ready signal.
Real-world Use Case
- The task is open-ended research where a short ReAct loop returns surface answers.
- A single model can hold the investigation’s working state and you want to avoid multi-agent coordination.
- Hundreds of tool calls are acceptable and budgeted.
Source
Advantages
- Single-agent simplicity avoids multi-agent coordination overhead.
- Explicit hypothesis revision gives a checkable place where drift becomes visible.
- Per-cycle compaction keeps context bounded even across hundreds of tool calls.
Disadvantages
- Long runs are expensive in tokens and wall-clock time.
- Compaction loses raw evidence; replay fidelity degrades over many cycles.
- Without strong termination criteria the loop devolves into Unbounded Loop.
- Single-agent self-revision shares all failure modes of Same-Model Self-Critique.