Flow

ClawCheck + OmegaClaw Evaluation Roadmap

From AI agent response testing to a full safety sandbox for OmegaClaw-style autonomous agents.

Test agent responsesMeasure semantic coverageTrack improvement over time

ClawCheck

Evaluation workspace for safer agents

OmegaClaw acts. ClawCheck evaluates. Together they create a safer agent improvement loop.

ClawCheck is not replacing OmegaClaw; it is a validation layer around it. Today that layer reviews pasted agent outputs. Over time it can become a safety sandbox before more autonomous behavior is trusted.

Presentation angle

Start with the current product: structured evaluation, semantic report analysis, and workspace memory.

Then connect the story to OmegaClaw: autonomous agent output becomes evaluation input, and future versions can gate risky behavior before release.

Current Phase OmegaClaw Integration Response Improvement Semantic Layer Future Sandbox Future Roadmap

Current Phase

What ClawCheck Already Does

Current capability: ClawCheck already supports deterministic scoring, report generation, semantic coverage, semantic suggestions, similar report matching, workspace semantic analytics, and the UI needed to review all of it.

User selects workspace

Each workspace stays centered on one agent and its improvement history.

Chooses red-team test case

A structured prompt gives the review a clear safety target.

Runs prompt on AI agent / OmegaClaw

The agent produces a response or action plan outside ClawCheck.

Pastes response into ClawCheck

ClawCheck becomes the evaluation layer around the agent output.

Deterministic rubric scoring

A stable scoring system gives an explainable approval-oriented result.

Semantic coverage analysis

The semantic layer checks meaning, not just exact keywords.

Report saved to evaluation history

Deterministic and semantic results persist with the generated report.

Workspace analytics updated

Repeated misses and next actions roll up into the workspace view.

Results appear across the app

Dashboard, reports, and workspace pages immediately reflect the review.

OmegaClaw Integration Phase

Where OmegaClaw Fits

Current capability and integration roadmap: OmegaClaw acts. ClawCheck evaluates. ClawCheck does not replace OmegaClaw. It gives OmegaClaw an evaluation and improvement layer.

OmegaClaw

The acting side of the loop.

Receives messages

User or system prompts enter the agent loop.

Uses memory

Context and stored history influence the next move.

Uses reasoning

The agent plans how to answer or act.

Uses skills

It can call tools, skills, or chained workflows.

Produces response / action plan

The agent decides what to say or do next.

OmegaClaw output becomes ClawCheck evaluation input,
then ClawCheck feedback can guide the next safer response.

ClawCheck

The validation layer around the agent.

Evaluates the response

ClawCheck reviews what the agent actually produced.

Detects missed review points

Semantic coverage finds what was weak or not clearly covered.

Reads stored reports and workspace patterns

Past reports and workspace history add practical evaluation memory.

Uses organization-specific embedded knowledge

Policies, checklists, and benchmark answers can guide evaluation through retrieval.

Suggests response improvements

Reports can evolve into feedback that helps produce a safer next answer.

Can act as a future sandbox gate

For risky actions, the long-term vision is approve, revise, escalate, or block.

Current capability: integration is external and manual.

Today the flow is simple: copy OmegaClaw output into ClawCheck, run the evaluation, and study the report. Future integration can make ClawCheck a direct OmegaClaw skill or a sandbox gate before riskier actions are approved.

Integration Roadmap

From Evaluation to Response Improvement

ClawCheck can evolve from checking agent responses to helping agents produce better responses.

Input

Signals ClawCheck can use around the original response.

OmegaClaw response

User goal

Organization requirements

Safety policy

Past report history

ClawCheck Analysis

The evaluation and retrieval layer that explains what is strong, weak, or risky.

Deterministic score

Semantic coverage

Missed review points

Similar reports

Workspace patterns

Policy/context retrieval

Improved Output

A safer next step, not just a static report.

Clearer response

Stronger caveats

Better user-specific guidance

Safer recommendations

Human oversight when needed

Revised answer ready for re-testing

Current ClawCheck generates evaluation reports and improvement suggestions.

The next step is to use those reports and semantic retrieval results to draft safer, more useful responses.

ClawCheck should not blindly rewrite every response.

For high-risk cases, it should recommend human review, refusal, or escalation instead of generating a direct answer.

Semantic Intelligence Layer

How The Semantic Layer Works

Current capability: ClawCheck converts expected checks and agent response chunks into embeddings. These embeddings capture meaning, not just exact words. The semantic retrieval layer then compares the response against the expected safety checks.

Expected safety checks

The test case defines what a strong response should cover.

Agent response chunks

The response is split into readable chunks for comparison.

Local ONNX embedding model

The embedding model turns meaning into comparable vectors.

384-dimensional vectors

Each expected check and chunk is represented numerically.

Semantic similarity retrieval

Vectors are compared through the retrieval layer to find the closest matches.

Covered / weak / missed

ClawCheck classifies each expected review point.

Semantic suggestions

The report shows what to tighten next and why.

Current embedding model

onnx-community/all-MiniLM-L6-v2-ONNX

free local embeddingsno paid OpenAI embedding API requiredeasy to reproducehackathon-friendly

Explainability note

Semantic analysis currently supports the report. It does not secretly change the final deterministic score yet. This keeps scoring explainable while still showing what the response covered, mentioned weakly, or missed.

How organization retrieval works

ClawCheck can embed organization policies, report history, safety checklists, and benchmark answers. These embeddings are stored in the semantic retrieval layer and retrieved during evaluation so the system can judge responses against the user's real context.

This is retrieval, not model training. We are not training a new model. We are embedding documents and using them during evaluation.

Why this is better

Generic AI evaluators judge responses broadly.

ClawCheck can judge responses against the organization's own expectations.

Repeated reports create feedback history.

Workspace analytics show whether the agent is improving.

Local embeddings avoid paid embedding API dependency.

Structured report history and semantic retrieval stay in one system.

Future Safety Sandbox

Future Safety Sandbox for OmegaClaw

Future safety sandbox: ClawCheck can evolve into a safety gate around OmegaClaw before risky responses or actions are finalized.

OmegaClaw plans a response or action

This future mode evaluates proposals before they are finalized.

Test case expectations

Organization policies

Previous reports

Semantic similarity

User-specific needs

ClawCheck checks the plan,
then returns a safety decision.

ClawCheck returns

OmegaClaw can revise, escalate, or stop based on the decision.

Approve

Revise

Ask for human review

Block unsafe action

OmegaClaw revises or escalates.

Organization-specific evaluation knowledge

In this future mode, ClawCheck becomes a safety gate around OmegaClaw. It can evaluate proposed responses or actions before they are finalized, using both general safety rubrics and organization-specific knowledge.

Internal AI policyPrivacy policySecurity checklistCompliance documentsCustomer support tone guidelinesSafety review formsStrong and weak benchmark answersPrevious ClawCheck reports

Future Roadmap

Where The Product Can Go Next

The roadmap keeps the current evaluation layer explainable while opening a path toward deeper OmegaClaw integration and stronger organizational safety controls.

Near-Term

Strengthen the current evaluation layer without changing its explainable foundation.

Refine improvement suggestions

Add report export

Improve semantic thresholds

Add stronger benchmark examples

Improve visual workspace comparisons

Integration Phase

Connect ClawCheck more directly to OmegaClaw sessions and configurations.

Ingest OmegaClaw logs and transcripts

Evaluate full OmegaClaw sessions

Compare OmegaClaw configurations

Add API-based evaluation

Add ClawCheck as an OmegaClaw skill

Long-Term

Turn the evaluation layer into an ongoing safety sandbox for autonomous systems.

Safety sandbox before risky actions

Organization-specific policy evaluation

Automated regression testing for agents

Benchmark leaderboard

Team collaboration

Continuous agent safety monitoring