OpenClaw Codex-GPT5.4 Task Validation Loop Issue

Task Execution Failure Mode in Autonomous Agent Workflows

A developer using Codex-GPT5.4 through OpenClaw for long-running autonomous project work reports a recurring failure mode where the model correctly identifies the next task, validates it, restates it, updates the task tracker, but then keeps repeating this process instead of actually executing the task.

The failure pattern specifically involves: detecting the correct next actionable task, rewriting/confirming it in the task file, acknowledging it in the next heartbeat/check-in, repeating the same acknowledgement, and still not performing the real implementation step. This creates a meta-loop around task validation rather than task execution.

Implemented Workspace Controls

To reduce this issue, the developer built an explicit workspace control layer around the model:

• TASKS.md: Acts as the single operational source of truth for active project, next autonomous task, next human-needed task, discoveries from previous rounds, and task state/prioritization. This prevents the model from "thinking from scratch" every time and forces continuity.
• Strong heartbeat rules: Added a dedicated heartbeat policy that explicitly states: reading/updating TASKS.md alone does not count as progress, each heartbeat round must execute at least one concrete action, repeated blockers without different attempts are forbidden, if NEXT_AUTO is executable it must be executed immediately, and the agent must not keep re-announcing the same blocker or same next step.
• Persona/execution contract files: Workspace-level instruction files to shape behavior including: execution style and anti-filler rules, user preferences and collaboration mode, session startup continuity, heartbeat behavior, and memory files for short-term and long-term continuity. These rules explicitly try to suppress patterns like: "I will do X" without actually doing X, repeating stable blockers, stopping after planning when execution is already possible, and revalidating the same next step over and over.
• Persistent memory + project notes: Includes long-term memory, daily memory, and project checkpoints/incident notes/debug reports for continuity.

Persistent Execution Loop Problem

Even with all this structure, the model can still drift into a loop where it acknowledges the next task has been identified, the task tracker is cleaned, the next step is clear, the next real step is X, and it's continuing autonomously—but no actual implementation starts. The model remains stuck in a control-plane loop instead of switching into the execution plane.

The developer notes the model is often good at diagnosis, prioritization, producing reasonable execution plans, and maintaining structured notes, but fails at crossing the boundary from validated intent to concrete action. Once in this pattern, it can keep consuming rounds restating the same thing in slightly different words.

The developer seeks solutions that work for long-running autonomous sessions, persistent task files, periodic heartbeat/check-in execution, and coding/debugging workflows where the agent is supposed to continue on its own.