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AI Design Blueprint Doctrine

Validate Agent Architecture (Consensus Mode)

architect.validate_consensus

Pro/Teams — N-shot CONSENSUS doctrine review of agentic code. ON CLIENT TIMEOUT — DO NOT RETRY THIS TOOL. Long-running (~80-120s for N=3 parallel LLM calls); MCP clients often close the call before the server returns. Retrying re-runs N × 60-180s LLM calls from scratch and burns N× compute. RECOVERY: same heartbeat pattern as architect.validate — the run_id is emitted in the FIRST progress event at t=0s (before LLM children fire); on timeout, call me.validation_history(run_id='<that-id>') to fetch the persisted consensus envelope. Runs N parallel architect.validate calls with private_session=True, then aggregates them to a per-principle MODE verdict + median severity + per-principle stability + score range/stdev. Returns one ConsensusValidationResponse with the headline median score, the honest variance band, and a representative full ValidationResponse (the child whose score is closest to the median). WHEN TO CALL: the user wants an HONEST first-pass score on agentic code, with the architect's variance surfaced. The single-shot architect.validate re-asserts the prior persisted run's verdict via baseline-anchor injection — same code can score 60/C anchored vs 98/A unanchored. Consensus mode is the unanchored honest read. WHEN NOT TO CALL: when you NEED the iteration delta against a prior run (regressions/improvements panel) — for that, call architect.validate which keeps baseline injection on. CHAIN RESUME: each child runs with private_session=True (no anchor) on purpose, but the CONSOLIDATED outer row IS persisted with lifecycle_status='completed' — the next single-shot architect.validate on the same repository auto-resolves it as prior_run_baseline. Consensus checkpoint becomes the new anchor. See the architect-validation-orchestration skill in the agent-asset pack for the full validate → consensus → certify sequence. BEHAVIOR: N (default 3, max 5) parallel LLM calls run concurrently; wallclock ~80-120s for N=3 (max child latency, not sum). Cost = N × LLM bill. Each child runs with private_session=True so the doctrine prompt's prior-run baseline injection is suppressed (no anchor bias). One CONSOLIDATED UserValidationRun row is written carrying the consensus envelope; the N children themselves do NOT persist (private_session contract). AUTH: Bearer , Pro/Teams plan. Same paid-plan gate as architect.validate. INPUTS: same shape as architect.validate. n is the only extra arg (range 2..5). private_session is implicit (always true for children); the OUTER consolidated row IS persisted unless the tool itself is called inside another private context — but no such wrapper exists today. OUTPUT: response carries score_consensus_median (headline), score_stdev (honest uncertainty), score_range (min, max), mode_stability_min_pct (the cert-eligibility gate's input — ≥ 80% means the consensus is stable), per_principle (mode + distribution + severity median per principle), and representative_response (the closest-to-median child's full ValidationResponse so existing UI components render unchanged). TYPED FAILURES: same as architect.validate (timed_out, rate_limited, dependency_unavailable). Plus consensus-specific: consensus_quorum_failed when fewer than 2 child runs succeeded (≥ 2 required to compute a meaningful median).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nNoNumber of parallel child runs. Default 3 (the variance signal is visible at N=3; cost = 3× LLM bill). Capped server-side by Settings.consensus_n_max (default 5).
taskNoWhat the agent or workflow is trying to accomplish.
filesNoList of file paths relevant to the implementation.
goalsNoSpecific safety or quality goals to evaluate against.
languageNoProgramming language of the code (e.g. 'python').
focus_areaNoOptional: narrow the review to a principle cluster or slug.
repositoryNoIteration key. Consensus children all run unanchored (`private_session=True`), but the consolidated row IS persisted under this key — discoverable as prior baseline for the next single-shot `architect.validate`. Same value across calls keeps the iteration arc inspectable.
example_limitNoMax curated examples per child run.
implementation_contextYesThe artifact under review. SEND FULL FILE CONTENTS VERBATIM — same constraint as architect.validate. Truncation produces hallucinated findings on code that isn't there.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.8/5.0
Behavior5/5

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

Disclosures are exceptionally thorough: long-running (~80-120s for N=3), client timeout handling, retry dangers (re-runs N×60-180s), recovery pattern, parallelism (N parallel calls with private_session), persistence model (child runs not persisted, outer row persisted), cost implications (N × LLM bill), auth (Bearer token, Pro/Teams plan), typed failures including consensus-specific quorum_failed. No annotations are contradicted (readOnlyHint=false, destructiveHint=false, idempotentHint=false are consistent with the long-running, non-idempotent, non-read-only nature).

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

Conciseness4/5

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

The description is lengthy but well-structured with clear sections (purpose, timing, recovery, when to call, behavior, etc.). It is front-loaded with the most critical information (timeout warning, recovery). Every section adds value; no fluff. Could be slightly trimmed but acceptable for the 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?

Given the tool's complexity (9 parameters, 1 required, output schema exists), the description covers all necessary context: input shape, expected behavior, output structure, error types, chain resume with architect.validate, and certification eligibility. It leaves no major gaps for an AI agent to operate correctly.

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% (all 9 parameters have descriptions), so baseline is 3. The description adds valuable context beyond schema: e.g., for `n` it explains default 3 provides variance signal and cost cap, for `repository` it explains iteration key and persistence semantics, for `implementation_context` it warns against truncation. This extra context raises the score to 4.

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 performs an N-shot consensus review of agentic code, using parallel architect.validate calls with private_session to remove baseline injection. It explicitly distinguishes itself from the sibling architect.validate (which uses baseline anchor), so an agent can select the right tool.

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?

Provides explicit guidance: 'WHEN TO CALL' (user wants an honest first-pass score with variance) and 'WHEN NOT TO CALL' (when iteration delta against prior run is needed, then use architect.validate). Also mentions alternative recovery via me.validation_history.

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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TDQS

A4.6/5.0
Disambiguation5/5

Each tool targets a clearly distinct purpose. For example, architect.validate vs architect.validate_consensus differ in single-shot vs consensus; handoffs.agency, handoffs.operator, and handoffs.partnership are separated by engagement type. No significant overlap.

Naming Consistency4/5

Tools follow a consistent dot-notation grouping (architect.*, clusters.*, examples.*, guides.*, handoffs.*, me.*, principles.*, signals.*, team.*) with predictable verbs (validate, list, get, search, add, etc.). Minor deviation: some underscore within names (e.g., me.add_evidence) but overall pattern holds.

Tool Count4/5

24 tools is on the higher side but justified given the broad domain spanning validation, certification, learning, handoffs, and feedback. Each tool has a clear role, and the count reflects the platform's comprehensive scope without feeling bloated.

Completeness5/5

The tool surface covers the full workflow (validate → consensus → certify), discovery (principles, clusters, examples, guides), personal progress (learning path, coaching, evidence), handoffs (support, partnership, agency), feedback, and team summaries. No obvious gaps for the stated purpose.

Resources