openclaw/docs/help/gpt54-codex-agentic-parity.md

GPT-5.4 / Codex Agentic Parity in OpenClaw

OpenClaw already worked well with tool-using frontier models, but GPT-5.4 and Codex-style models were still underperforming in a few practical ways:

• they could stop after planning instead of doing the work
• they could use strict OpenAI/Codex tool schemas incorrectly
• they could ask for /elevated full even when full access was impossible
• they could lose long-running task state during replay or compaction
• parity claims against Claude Opus 4.6 were based on anecdotes instead of repeatable scenarios

This parity program fixes those gaps in four reviewable slices.

What changed

PR A: strict-agentic execution

This slice adds an opt-in strict-agentic execution contract for embedded Pi GPT-5 runs.

When enabled, OpenClaw stops accepting plan-only turns as “good enough” completion. If the model only says what it intends to do and does not actually use tools or make progress, OpenClaw retries with an act-now steer and then fails closed with an explicit blocked state instead of silently ending the task.

This improves the GPT-5.4 experience most on:

• short “ok do it” follow-ups
• code tasks where the first step is obvious
• flows where update_plan should be progress tracking rather than filler text

PR B: runtime truthfulness

This slice makes OpenClaw tell the truth about two things:

• why the provider/runtime call failed
• whether /elevated full is actually available

That means GPT-5.4 gets better runtime signals for missing scope, auth refresh failures, HTML 403 auth failures, proxy issues, DNS or timeout failures, and blocked full-access modes. The model is less likely to hallucinate the wrong remediation or keep asking for a permission mode the runtime cannot provide.

PR C: execution correctness

This slice improves two kinds of correctness:

• provider-owned OpenAI/Codex tool-schema compatibility
• replay and long-task liveness surfacing

The tool-compat work reduces schema friction for strict OpenAI/Codex tool registration, especially around parameter-free tools and strict object-root expectations. The replay/liveness work makes long-running tasks more observable, so paused, blocked, and abandoned states are visible instead of disappearing into generic failure text.

PR D: parity harness

This slice adds the first-wave QA-lab parity pack so GPT-5.4 and Opus 4.6 can be exercised through the same scenarios and compared using shared evidence.

The parity pack is the proof layer. It does not change runtime behavior by itself.

Why this improves GPT-5.4 in practice

Before this work, GPT-5.4 on OpenClaw could feel less agentic than Opus in real coding sessions because the runtime tolerated behaviors that are especially harmful for GPT-5-style models:

• commentary-only turns
• schema friction around tools
• vague permission feedback
• silent replay or compaction breakage

The goal is not to make GPT-5.4 imitate Opus. The goal is to give GPT-5.4 a runtime contract that rewards real progress, supplies cleaner tool and permission semantics, and turns failure modes into explicit machine- and human-readable states.

That changes the user experience from:

• “the model had a good plan but stopped”

to:

• “the model either acted, or OpenClaw surfaced the exact reason it could not”

Architecture

flowchart TD
    A["User request"] --> B["Embedded Pi runtime"]
    B --> C["Strict-agentic execution contract"]
    B --> D["Provider-owned tool compatibility"]
    B --> E["Runtime truthfulness"]
    B --> F["Replay and liveness state"]
    C --> G["Tool call or explicit blocked state"]
    D --> G
    E --> G
    F --> G
    G --> H["QA-lab parity pack"]
    H --> I["Scenario report and parity gate"]

Scenario pack

The first-wave parity pack currently covers four scenarios:

approval-turn-tool-followthrough

Checks that the model does not stop at “I’ll do that” after a short approval. It should take the first concrete action in the same turn.

model-switch-tool-continuity

Checks that tool-using work remains coherent across model/runtime switching boundaries instead of resetting into commentary or losing execution context.

source-docs-discovery-report

Checks that the model can read source and docs, synthesize findings, and continue the task agentically rather than producing a thin summary and stopping early.

image-understanding-attachment

Checks that mixed-mode tasks involving attachments remain actionable and do not collapse into vague narration.

Release gate

GPT-5.4 can only be considered at parity or better when the merged runtime passes the parity pack and the runtime-truthfulness regressions at the same time.

Required outcomes:

• no plan-only stall when the next tool action is clear
• no fake completion without real execution
• no incorrect /elevated full guidance
• no silent replay or compaction abandonment
• parity-pack metrics that are at least as strong as the agreed Opus 4.6 baseline

Who should enable strict-agentic

Use strict-agentic when:

• the agent is expected to act immediately when a next step is obvious
• GPT-5.4 or Codex-family models are the primary runtime
• you prefer explicit blocked states over “helpful” recap-only replies

Keep the default contract when:

• you want the existing looser behavior
• you are not using GPT-5-family models
• you are testing prompts rather than runtime enforcement