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aidesignblueprint

AI Design Blueprint Doctrine

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architect.validate

Validate agentic workflows against the 10-principle AI Blueprint. Get a readiness score, per-principle findings, and production-ready badge eligibility.

Instructions

Pro/Teams — first-pass doctrine review of agentic code/workflow against the 10-principle Agentic AI Blueprint. Returns code_classification (autonomous_agentic_workflow vs non_agentic_component), per-principle findings (verdict, severity_score 0-100, severity_class, code-cited evidence, recommendation), severity-weighted readiness (score|null, grade|null, tier ∈ {production_ready, emerging, draft, not_applicable}), recommended examples, reproducibility envelope (model, seed, doctrine_fingerprint, prompt_template_fingerprint), persistence_status with shareable run_id/badge_url/review_url. WHEN TO CALL: the user wants a governance audit, readiness score, or production_ready badge on an agent/workflow they just built or changed. WHEN NOT TO CALL: non-agentic plumbing (math utilities, type aliases, event-loop helpers, single-shot request/response handlers) returns tier=not_applicable with score=null/grade=null — that's not a failure, the doctrine simply doesn't grade non-agentic code, and architect.certify will refuse with not_agentic_component. Submit the OWNING agentic workflow instead. BEHAVIOR: long-running LLM call (~60-180s typical at high reasoning effort, single-pass; server-side budget 20 min). Mints run_id at t=0; first notifications/progress event carries run_id as recovery handle; keepalive every 30s. Persists ValidationRun + UserValidationRun + AIValidationRunLog + LLMUsageLog atomically; on rollback, badge/review URLs are stripped. Auth: Bearer , Pro/Teams plan. UK/EU residency; transient OpenAI processing (no-training); prompt-injection in code is inert. INPUTS: send FULL file contents verbatim as implementation_context (NO truncation, NO ... placeholders, NO comment removal — the architect treats your ... as literal code and hallucinates bugs that don't exist). If too large, split into MULTIPLE calls scoped by file/module; never truncate one call. Pass repository="" to group runs into a project trend. Pass private_session=true to bypass server-side logging (persistence + recovery disabled). focus_area narrows scope; unmatched focus_area fails explicitly rather than silently widening. RECOVERY: if your MCP client closes the tool-call early, fetch the result via me.validation_history(run_id=) once the run completes server-side — same Bearer token (per-user auth). Unavailable when private_session=true. TYPED FAILURES: timed_out, rate_limited, dependency_unavailable, schema_mismatch (each carries retryable + next_action). NEXT STEP: if tier=production_ready (A or B grade), the response carries certification_status='not_evaluated' — call architect.certify(run_id, code) to mint the certified production_ready badge (separate ~60-150s adversarial review, eligibility-gated).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskNoWhat the agent or workflow is trying to accomplish. Adds evaluation context.
filesNoList of file paths relevant to the implementation context.
goalsNoSpecific safety or quality goals to evaluate against (e.g. 'prevent irreversible actions', 'explicit approvals').
languageNoProgramming language of the code being evaluated (e.g. 'python', 'typescript').
focus_areaNoNarrow the evaluation to a specific principle cluster or slug (e.g. 'delegation', 'visibility', 'establish-trust-through-inspectability').
repositoryNoRepository name or path for additional context.
example_limitNoMaximum number of curated examples to include in recommendations.
private_sessionNoSet to true to disable all logging for this validation call.
implementation_contextYesThe artifact under review. SEND FULL FILE CONTENTS VERBATIM — the architect cites per-line evidence (identifiers, branch ordering, structural choices); any compression destroys evidence and produces hallucinated findings on code that isn't there. CONCRETE DON'TS: do NOT replace docstrings/comments with `...`; do NOT condense multi-line statements; do NOT replace dict/set comprehensions with `{...}`; do NOT remove explanatory comments to save tokens. If the file is large, split into MULTIPLE architect.validate calls scoped by file/module — never truncate one call. Architecture summaries (high-level prose) accepted ONLY for greenfield (no code yet); never as a substitute for code that already exists.
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description extensively discloses behavioral traits: long-running LLM call (~60-180s, up to 20 min), run_id creation, persistence, auth requirements, residency, and error types. This goes far beyond the annotations (readOnlyHint, openWorldHint, idempotentHint) and provides critical context for the agent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear section headings (WHEN TO CALL, BEHAVIOR, INPUTS, etc.). It is comprehensive yet front-loaded with purpose. Every sentence provides necessary information without redundancy. The length is appropriate for the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having no output schema, the description thoroughly explains the return structure (code_classification, findings, readiness, etc.). It covers error handling, recovery, and next steps. The description is complete for the tool's complexity and sibling context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, but the description adds significant value for key parameters, especially 'implementation_context' with detailed instructions on sending full contents. It also clarifies 'private_session' and 'focus_area' behavior. While not all parameters gain extra context, the added guidance for critical ones raises the score above baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'first-pass doctrine review of agentic code/workflow against the 10-principle Agentic AI Blueprint.' It specifies the return structure and distinguishes from sibling tools like architect.certify by outlining when to call each. The purpose is specific and actionable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit 'WHEN TO CALL' and 'WHEN NOT TO CALL' sections provide clear usage guidance. It tells the agent exactly when to use this tool (governance audit, readiness score) and when not to (non-agentic plumbing, which returns tier=not_applicable). It also suggests alternatives like architect.certify for badges and splitting large inputs into multiple calls.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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