Observed Signal · Jun 13, 2026 · Technical Deep Dive · Source: DEV Community · Impact: 2/5 · Sentiment: Neutral
AIClaw Keeps Agent Plans Out of Chat History
The post describes AIClaw's existing design for handling agent planning using a Runtime Plan State that is owned by the execution harness rather than stored as assistant text in chat. The model may propose or revise plans, but a PlanManager normalizes and validates changes, the executor persists and streams a compact plan snapshot, and only a terse <plan_state> block (goal, current running step, pending summary, revision reason, numeric progress) is injected into each LLM prompt. Plans follow a small lifecycle (pending → running → completed/failed/blocked; pending → skipped), enabling the harness to enforce single-running-step rules and keep execution logs, streaming progress, and final assistant responses separate. The design aims to improve chat UX and observability for tool-using agents. AIClaw is open-source at github.com/chowyu12/aiclaw.
Describes a concrete agent architecture pattern that improves UX and execution observability for tool-using LLM agents; relevant to developers and platform engineers but not a major platform or industry-shifting announcement.
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Key Takeaways & Evidence Grounding
- AIClaw stores agent planning as a structured Runtime Plan State owned by the executor rather than as chat-visible assistant text.
- The model proposes plan changes; a PlanManager/harness normalizes, validates, persists and owns the active plan state.
- Plan lifecycle states in AIClaw include: pending -> running -> completed | failed | blocked, and pending -> skipped.
- AIClaw injects a compact <plan_state> block (goal, current running step, short pending summary, latest revision reason, numeric progress) into each model call instead of the full plan history.
- AIClaw is open source and available at github.com/chowyu12/aiclaw.
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