Evolution Audit #1 – Baseline Establishment
Every Inc – 2026-04-01 Auditor: evolution-auditor skill, ai-first-org-design-kit Status: BASELINE – first audit, no prior data for comparison
Governance Health Metrics (Baseline)
This is Day 1 of governance operations. No decision ledger data exists yet. The following establishes target ranges that future audits will measure against.
| Metric | Baseline Value | Target Range | Action Threshold |
|---|---|---|---|
| Escalation rate | No data (Day 1) | <20 escalations/month per domain | >20/domain/month = governance gap or agent miscalibration |
| Tier distribution | No data (Day 1) | Tier 1: 65-90% of all decisions; Tier 2: 5-20%; Tier 3: 3-10%; Tier 4: <3% | Tier 1 <60% = agents too cautious; Tier 1 >95% = agents too autonomous |
| Human override rate (Tier 2) | No data (Day 1) | <25% override rate per decision type | >25% = authority tier likely wrong; tighten to Tier 3 |
| Tier 2 success rate | No data (Day 1) | >90% success | <80% = tighten to Tier 3 or improve agent capability |
| Tier 3 human response time | No data (Day 1) | <12h average | >12h = too many Tier 3 decisions or approver overloaded |
| Boundary proximity events | No data (Day 1) | <3 per boundary per month | >3/boundary/month = boundary needs clarification or workflow redesign |
| First-pass gate approval rate | No data (Day 1) | See Gate Effectiveness below | Per-gate targets set below |
| Policy generation rate | No data (Day 1) | 0-2 candidate policies/month (initial expectation) | If 0 after 60 days with active operations: governance may be too loose or agents not logging escalations |
| Novel situation frequency | No data (Day 1) | High initially (5-15/month), declining over 90 days | If still >10/month at 90 days: governance is lagging behind operations |
Interpretation Notes for First Review (May 2026)
The first 30 days will produce noisy data. Expect:
- High novel-situation escalation rate (agents encountering governance for the first time)
- Low Tier 1 ratios (agents will be cautious until they calibrate)
- Variable human response times (approvers learning new escalation workflows)
Do not make governance changes based on first-month data alone. Observe, log, and wait for the second month to identify real patterns vs. startup noise.
Gate Effectiveness Assessment
Gate 1: Article Publication
| Dimension | Assessment | Rating |
|---|---|---|
| Well-defined? | Yes. 5 Tier 1 criteria (blocking) + 3 Tier 2 criteria (advisory) with clear pass/fail language. | STRONG |
| Criteria testable? | Mostly. Criteria 1-4 (thesis, AI tells, experience grounding, voice) are testable with specific markers. Criterion 5 (length/structure) is the weakest – “minimum depth for the topic” is subjective without a word-count floor or topic-complexity rubric. | GOOD with caveat |
| Holdout coverage | Comprehensive. 7 scenarios covering: AI slop, missing thesis, theory without practice, legitimate good content, news recap, contrarian views, case study framing. | STRONG |
| Expected false positive rate | 5-10%. Risk area: criterion 2 (AI tells) may flag legitimate use of transitional phrases that happen to overlap with AI tells list. Scenario 4 acknowledges this – one “It’s worth noting” should not fail a Dan Shipper piece. | LOW-MEDIUM |
| Expected false negative rate | 5-8%. Risk area: criterion 3 (experience grounding) can be gamed with fabricated experience. Scenario 1 explicitly tests this but detection depends on the agent’s ability to cross-reference claimed experiences against known Every work. | LOW-MEDIUM |
| Satisfaction target | 90% of gate-passing articles should also pass Kate’s manual review. Appropriate for an editorial gate. | APPROPRIATE |
First calibration recommendation: Add a specificity floor to criterion 5 – “meets minimum depth” should reference the quality standards in genome/02-quality-standards/BY-OUTPUT-TYPE.md for concrete thresholds. Currently, an agent could pass a thin article if it technically has beginning/middle/end. Route to: quality-gate-designer.
Gate 2: Code Merge
| Dimension | Assessment | Rating |
|---|---|---|
| Well-defined? | Yes. 4 Tier 1 criteria (blocking) + 2 Tier 2 criteria (blocking) + 3 Tier 3 criteria (advisory). Clear separation between automated checks and human judgment. | STRONG |
| Criteria testable? | Yes. Criteria 1-4 are objectively verifiable (tests pass, P1 resolved, plan exists, core flows work). Criterion 5 (compound artifact) is verifiable by checking for the artifact’s existence. Criterion 6 (findings triaged) is traceable in the review system. | STRONG |
| Holdout coverage | Good. 6 scenarios covering: missing compound, vibe coding, hotfix override, ignored P1, performance tradeoff, new dependency. | GOOD |
| Expected false positive rate | <3%. Criteria are objective and verifiable. The main risk is criterion 5 (compound artifact) flagging trivial PRs that genuinely don’t warrant documentation – a one-line typo fix doesn’t need a docs/solutions/ entry. | LOW |
| Expected false negative rate | <5%. The 14-agent parallel review is already proven. Main gap: criterion 3 (plan adherence) trusts that a plan exists but doesn’t verify plan quality. A bad plan that is faithfully implemented would pass. | LOW |
| Satisfaction target | 95% of gate-passing PRs ship without rollback within 48h. Ambitious but appropriate for a mature compound engineering system. | APPROPRIATE |
First calibration recommendation: Add a compound-artifact waiver for trivial changes (e.g., PRs under N lines that are pure bugfixes with no new patterns). This prevents the gate from creating busywork that undermines “ship and iterate.” The waiver should still require the GM to confirm the change is genuinely trivial. Route to: quality-gate-designer.
Gate 3: Consulting Deliverable
| Dimension | Assessment | Rating |
|---|---|---|
| Well-defined? | Yes. 4 Tier 1 criteria (blocking) + 3 Tier 2 criteria (blocking) + 2 Tier 3 criteria (advisory). Builder credibility and confidentiality are properly prioritized as Tier 1. | STRONG |
| Criteria testable? | Mostly. Criteria 1-4 are testable (grounded in experience, known tools, no overselling, no cross-client data). Criterion 5 (client-specific customization) is harder to test automatically – “tailored to the client’s AI maturity level” requires context that may not be fully available to the gate agent. Criterion 7 (hands-on component) depends on deliverable type. | GOOD with caveat |
| Holdout coverage | Good. 5 scenarios covering: generic roadmap, unfamiliar tools, cross-client data leak, excellent deliverable, overselling. | GOOD |
| Expected false positive rate | 8-12%. Risk area: criterion 2 (no unfamiliar tools) may be too strict for client-specific contexts where a non-Every tool is genuinely the right recommendation. Scenario 2 acknowledges this but relies on Natalia override – the gate itself will generate friction. | MEDIUM |
| Expected false negative rate | 5-8%. Risk area: criterion 4 (confidentiality) relies on the agent’s ability to detect implicit client identification in anonymized references. Subtle patterns like “a mid-market hedge fund in New York with 40 employees” may uniquely identify a client. | LOW-MEDIUM |
| Satisfaction target | Client NPS >70 across all engagements. Appropriate but lagging indicator – NPS won’t surface problems until weeks/months after gate decisions. | APPROPRIATE but slow |
First calibration recommendation: Criterion 2 (unfamiliar tools) should be refined to distinguish between “Every doesn’t use this tool” and “Every has evaluated and rejected this tool.” Recommending a tool Every hasn’t tried is a builder credibility issue; recommending a tool that is genuinely right for the client’s context (but not Every’s) may be appropriate with Natalia’s approval. Add a “client-context exception” path. Route to: quality-gate-designer.
Gate 4: Social Media Publication
| Dimension | Assessment | Rating |
|---|---|---|
| Well-defined? | Yes. 4 Tier 1 criteria (blocking) + 2 Tier 2 criteria (advisory). Simplest gate, appropriate for the output type. | STRONG |
| Criteria testable? | Yes. Criteria 1-4 (voice match, thesis capture, factual accuracy, no clickbait) are all testable against the source article. Criterion 6 (platform appropriateness) is the most subjective. | GOOD |
| Holdout coverage | Good. 5 scenarios covering: generic promotion, boring accuracy, good post, misrepresentation, platform mismatch. | GOOD |
| Expected false positive rate | 5-8%. Risk area: criterion 1 (author voice match) is subjective and may flag posts from new or guest authors whose voice is less well-established in the system. | LOW-MEDIUM |
| Expected false negative rate | <5%. The criteria target the most common social media anti-patterns effectively. | LOW |
| Satisfaction target | 85% of auto-generated posts require no edits from Anthony. Realistic given Anthony built the system himself. | APPROPRIATE |
First calibration recommendation: Criterion 6 (platform appropriateness) should be expanded with platform-specific sub-criteria. “Appropriate for X” vs. “appropriate for LinkedIn” is too vague for automated assessment. Add: X posts max 280 chars (or thread format), LinkedIn posts include a hook + paragraph structure, etc. Route to: quality-gate-designer.
Gate Architecture Overall Assessment
Strengths:
- All 4 gates have clear tier separation (blocking vs. advisory vs. informational)
- Holdout scenarios exist for every gate (7+6+5+5 = 23 total scenarios)
- Satisfaction metrics are defined for every gate with concrete thresholds
- Hard boundary alignment: gates enforce HB-1 (never publish without review), HB-5 (never merge without review), HB-8 (builder credibility), HB-9 (never bypass gates)
- Political risk is assessed per gate and mitigation strategies are documented
Gaps:
- No cross-gate consistency check. What happens when an article references a consulting engagement and needs both the editorial gate AND confidentiality checks from the consulting gate?
- No gate for podcast episode publication, despite it being listed as Tier 3 in the Authority Matrix (Rachel Braun approver). This is a gap.
- Holdout scenarios are static. No mechanism defined for adding new holdout scenarios as new failure modes are discovered in operations.
Recommendation: Add a podcast episode publication gate (even a lightweight one). Update INDEX.md to note the cross-gate scenario for articles referencing consulting work. Define a process for adding holdout scenarios based on real gate failures. Route to: quality-gate-designer.
Genome Alignment Check
Values Operationally Encoded?
| Value | Decision Rules? | Agent Instructions? | Conflict Resolution? | Assessment |
|---|---|---|---|---|
| Builder Credibility | Yes – “never recommend tools/practices we haven’t used” | Yes – “always ground claims in Every’s actual experience” | Yes – “always wins, absolute tiebreaker” | FULLY OPERATIONAL |
| Taste Over Process | Yes – “trust person with demonstrated taste over checklist” | Yes – “apply rigor tests and voice norms, not checklists” | Yes – “customer-facing: taste wins; internal: speed wins” | FULLY OPERATIONAL |
| Ship and Iterate | Yes – “ship v1 unless it touches customer-facing quality” | Yes – “default to shipping; core flow works = ship it” | Yes – “customer-facing content: taste wins; software: ship if core flow works” | FULLY OPERATIONAL |
| Generalist Advantage | Yes – “favor people who operate across domains” | Yes – “support cross-domain work, frame around full product outcomes” | Yes – “novel problems: generalist wins; well-defined technical: specialist wins” | FULLY OPERATIONAL |
| Play as Strategy | Yes – “choose playful/experimental over safe/professional” | Yes – “favor personality over formality” | Yes – “internal/content: play wins; legal/financial: professionalism wins” | FULLY OPERATIONAL |
Assessment: All 5 values have decision rules, agent instructions, real examples, what-we-sacrifice sections, and conflict resolution rules. This is exceptionally thorough. The priority ordering (builder credibility > taste > ship > generalist > play) is explicitly stated and consistently reflected across documents.
Anti-Patterns Specific Enough?
| Anti-Pattern | Specificity | Catchable by Agent? | Assessment |
|---|---|---|---|
| AI Slop | HIGH – lists specific markers (formulaic transitions, hedging, vague pronouns) | YES – pattern-matchable | STRONG |
| News Recap Without Thesis | HIGH – “summaries with no argument” with clear alternative | YES – testable against thesis criterion | STRONG |
| Corporate Blog Voice | HIGH – lists forbidden phrases with alternatives | YES – keyword/phrase detection | STRONG |
| Theory Without Practice | HIGH – “frameworks not grounded in real experience” | YES – check for experience markers | STRONG |
| Code Without Compound | HIGH – “no docs, no CLAUDE.md updates, no patterns” | YES – artifact existence check | STRONG |
| Vibe Coding | HIGH – “code without a plan” with Plan/Work/Review/Compound ratios | YES – plan.md existence check | STRONG |
| Consulting from PowerPoint | MEDIUM – “slide decks without hands-on building” | PARTIALLY – hard to detect in automated review | GOOD |
| Over-Standardization of GM Workflows | MEDIUM – describes the general pattern | PARTIALLY – meta-pattern, hard for agents to self-detect | GOOD |
| Scaling Consulting by Diluting | MEDIUM – hiring guidance | NO – Tier 4, human-only decision | N/A (correctly human-only) |
| Ignoring Cultural Functions When Encoding | HIGH – references dual-system classification from audit | PARTIALLY – requires context about the structure being encoded | GOOD |
Assessment: Anti-patterns are specific and well-grounded in Every’s actual failure modes. The first 6 are directly catchable by agents through the quality gates. The remaining 4 are meta-patterns that apply to organizational decisions rather than individual outputs – appropriately left for human judgment.
Voice Norms: Testable or Subjective?
| Voice Norm | Testable? | How? |
|---|---|---|
| Forbidden words (“leverage,” “synergy,” etc.) | YES | String matching |
| AI tells rejection list | YES | Pattern matching (Katie Parrott’s detection skills) |
| First-person requirement | YES | Pronoun detection |
| Formality gradient by context | PARTIALLY | Requires context classification first, then tone assessment |
| “Sounds like a specific author” | SUBJECTIVE | Cannot be fully automated – this is where human taste enters. Correctly gated behind Kate’s Tier 3 review for articles. |
| Three rigor tests | PARTIALLY | Criterion 1 (specific claim) is testable. Criterion 2 (learnable value) is semi-testable. Criterion 3 (author voice) is subjective. |
Assessment: Voice norms are a well-designed mix of testable markers (forbidden words, AI tells, structural requirements) and irreducibly subjective judgments (author voice, taste). The subjective elements are correctly routed to human reviewers rather than being faked with automated checks. This reflects the “taste over process” value – the norms encode what can be encoded and protect what cannot.
Gaps or [DRAFT] Markers?
No [DRAFT], [TODO], [TBD], or [PLACEHOLDER] markers found in any genome or governance document. All 7 genome files and all 7 governance files are complete v1.0 documents with review signatures.
One structural gap identified: The genome’s AUTHORITY-MATRIX.md and the governance’s AUTHORITY-MATRIX.md are separate files with overlapping content. The genome version is a values-integrated summary; the governance version is the operational specification. This is intentional (the AGENT-PRIMER.md references both) but creates a maintenance risk: if one is updated without the other during a learning loop cycle, they could diverge. Recommend adding a consistency check to the monthly review process. Route to: governance-architect.
Agent Census
Compound Engineering Agents
| Agent | Status | Domain | Notes |
|---|---|---|---|
| 14 review agents (parallel) | ACTIVE | Engineering (all products) | Run on every PR. Well-proven, core to compound engineering methodology. |
| Planning agents | ACTIVE | Engineering (all products) | PRD/plan creation assistance. Used by all GMs. |
| Work agents | ACTIVE | Engineering (all products) | Code generation within approved plans. Primary execution agents. |
| Compounding agents | ACTIVE | Engineering (all products) | Knowledge extraction from completed PRs. Produces docs/solutions/ entries. |
Personal Agents
| Agent | Human | Status | Level | Notes |
|---|---|---|---|---|
| R2-C2 | Dan Shipper | ACTIVE | Mature | Model for other personal agents. CEO’s operational assistant. |
| Iris | Anukshi Mittal | ACTIVE | Established | Product marketing workflows. |
| Montaigne | Austin Tedesco | ACTIVE | Established | Growth work and campaign strategy. |
| Margot | Katie Parrott | ACTIVE | Mature | AI tells detection, editorial quality work. Also assists Kate (EIC). |
| Alfredo | Lucas Crespo | ACTIVE | Established | Design workflow integration including Figma MCP. |
| Milo | Brandon Gell | ACTIVE | Established | Operational systems and infrastructure. |
Product Agents
| Agent/System | Product | Status | Notes |
|---|---|---|---|
| Spiral agents | Spiral (Danny Aziz) | ACTIVE | Multi-agent writing loops. Danny also uses Droid CLI. |
| Cora agents | Cora (Kieran Klaassen) | ACTIVE | Email processing and management. |
| Monologue agents | Monologue (Naveen Naidu) | ACTIVE | Voice transcription and structuring. 143K-line codebase. |
| Sparkle agents | Sparkle (Yash Poojary) | ACTIVE | File organization per user preferences. Yash also built AgentWatch. |
Consulting/Editorial Agents
| Agent | Domain | Status | Notes |
|---|---|---|---|
| Claudie | Consulting (Natalia Quintero) | ACTIVE | AI project manager. Saves 14 hrs/week. Proven at scale. Audit recommends expansion to all engagements. |
| Anthony’s Claude+X API system | Social media | ACTIVE | Custom-built social distribution pipeline. |
| Katie’s AI tells detection | Editorial | ACTIVE | AI writing pattern detection. Part of article publication pipeline. |
Plus One Agents
| Agent | Status | Notes |
|---|---|---|
| Plus One subscriber agents | IN ROLLOUT | OpenClaw-hosted. Answering subscriber questions in Slack. Tier 2 authority (responses based on approved knowledge base, flagged for review). New – limited operational history. |
Census Summary
- Total agent categories: 4 (compound engineering, personal, product, consulting/editorial)
- Named personal agents: 6
- Product agent ecosystems: 4
- Specialized agents: 3 (Claudie, Anthony’s system, Katie’s detection)
- New/in-rollout: 1 (Plus One)
- Agents with no governance entry: None detected. All agents map to the Authority Matrix agent-type categories.
Gap: Plus One agents are the newest and least-proven category. They operate in client Slack workspaces where boundary proximity events (HB-2: external communications, HB-4: client data) are structurally likely. Holdout scenario 1 in LEARNING-LOOP.md directly tests this. Recommend prioritizing Plus One decision logging in the first month of ledger operations. Route to: governance-architect.
Genome Fitness
| Value | Decision Rule | Fitness | Evidence | Action |
|---|---|---|---|---|
| Builder Credibility | Never recommend what you haven’t built | Healthy | All consulting deliverables grounded in Every’s practice; compound engineering plugin is the proof | None |
| Taste Over Process | Trust judgment over checklists; customer-facing → taste wins | Healthy | Three rigor tests operationally encoded; Kate retains veto; AI tells detection active | None |
| Ship and Iterate | Ship v1 if core flow works | Healthy | Spiral v3 shipped by one engineer; Proof built as side project; compound engineering enables rapid iteration | None |
| Generalist Advantage | Everyone blends roles; GMs run full products solo | Healthy | Two-Slice Team model operational; every GM handles product/eng/design/marketing | None |
| Play as Strategy | “Be sincere, not serious”; personality over formality | Healthy | Named agents (R2-C2, Iris, etc.); playful culture documented in voice norms | None |
Assessment: All 5 values are Healthy. No drift detected. The priority ordering (builder credibility > taste > ship > generalist > play) is consistently reflected across all governance, gate, and spec artifacts.
Policy-Spec Gap Analysis
| Ad-Hoc Policy | Classification | Root Cause | Route To |
|---|---|---|---|
| No formal policy for articles referencing consulting engagements (cross-gate) | New Policy | Articles about client work need both editorial and confidentiality checks; no gate handles the intersection | quality-gate-designer |
| No podcast publication gate exists despite Tier 3 classification | Gate Gap | Authority Matrix defines podcast as Tier 3 (Rachel approver) but no gate specification exists | quality-gate-designer |
| Plus One agent scope in client Slack not formally bounded beyond hard boundaries | Spec Gap | Plus One is new; specific behavioral specs for subscriber-facing Slack responses don’t exist yet | specification-writer |
| No policy for compound artifact waivers on trivial PRs | New Policy | Code merge gate requires compound artifact on every PR, but one-line typo fixes don’t warrant documentation | quality-gate-designer |
Authority Matrix Calibration
| Decision Type | Current Tier | Proposed Tier | Evidence | Risk |
|---|---|---|---|---|
| Bug auto-fix (compound engineering) | Tier 2 (Autonomous + Notify) | Keep Tier 2 | Proven workflow — “my AI had already fixed the code before I saw it.” No evidence of problems. | Low — well-tested pattern |
| Social media draft generation | Tier 2 (Autonomous + Notify) | Keep Tier 2 | Anthony built the system; generation is safe, posting is still Tier 3 | Low |
| Plus One subscriber responses | Tier 2 (Autonomous + Notify) | Consider Tier 3 (Human-in-Loop) for first 90 days | New category, operating near HB-2 (external comms) and HB-4 (client data) boundaries | Medium — untested in production |
| Article publication | Tier 3 (Human-in-Loop, Kate) | Keep Tier 3 | Editorial quality is Every’s brand; no case for loosening | None — this should remain Tier 3 permanently |
| Cross-product data sharing | Tier 3 (Default: Deny) | Keep Tier 3 (Deny) | Vision exists (Cora→Spiral) but implementation not ready; premature loosening risks PII issues | Low — keep conservative |
Baseline note: No operational data exists for calibration decisions yet. These assessments are structural, based on the design. Real calibration begins with the May 2026 review when decision ledger data is available.
Adoption Maturity Snapshot
Distribution
| Level | Count | % of Team | Members |
|---|---|---|---|
| L3 – Transformative | 9 | 47% | Dan Shipper, Katie Parrott, Kieran Klaassen, Naveen Naidu, Yash Poojary, Danny Aziz, Natalia Quintero, Anthony Scarpulla, (Kate Lee trending) |
| L2 – Adoptive | 7 | 37% | Brandon Gell, Kate Lee, Andrey Galko, Nityesh Agarwal, Brooker Belcourt, Lucas Crespo, Austin Tedesco, Anukshi Mittal, Rachel Braun |
| L1-2 – Transitional | 2 | 11% | Eleanor Warnock, Jack Cheng |
| L1 – Capable | 1 | 5% | (Eleanor counted in L1-2) |
| L0 – Not Engaged | 0 | 0% | None |
Organizational mean: 2.4 – Exceptionally high. No one at L0. The floor is L1.
Note: The maturity ladder assessment counted 19 individuals in some sections and used slightly different groupings. The distribution above reflects the summary from the maturity-ladder skill output: 9 at L3, 7 at L2, 2 at L1-2, 0 at L0.
Priority Progressions
Highest ROI (L2 to L3):
- Kate Lee – Unblocks editorial pipeline bottleneck. Build shareable editorial quality gate for writers.
- Brandon Gell – Improves cross-product engineering infrastructure. Build shared operational tool adopted by GMs.
- Lucas Crespo – Reduces 40 hrs/month design coordination overhead. Build design request intake system.
- Brooker Belcourt – Strengthens finance consulting credibility and capacity. Build auditable finance AI workflow.
Critical Floor-Raising (L1 to L2):
- Eleanor Warnock – Design one reusable editorial pipeline workflow. Buddy pairing with Katie Parrott recommended.
- Jack Cheng – Design one reusable AI writing workflow. Buddy pairing with Katie Parrott for one article cycle.
Key Risk: The Level 2 Plateau
Seven team members at L2 may find it “good enough.” The jump to L3 requires building something others use, which demands different skills (tool design, documentation, evangelism). Watch for signals: “My workflow works great for me” without sharing; using AI tools without extending them; declining demos.
Sprint Status
Adoption sprint not yet run. The maturity-ladder skill recommends running adoption-sprint-designer to design a sprint targeting the L2-to-L3 transition for the 7 team members at Level 2.
Recommendations (Ranked)
| Priority | Finding | Evidence | Route To | Action |
|---|---|---|---|---|
| P1 | Missing podcast publication gate | Authority Matrix lists Tier 3 (Rachel approver) but no gate spec exists | quality-gate-designer |
Create podcast-publication gate with builder credibility screen + production quality criteria |
| P1 | Plus One governance tightening for launch | Plus One agents operate in client Slack near HB-2 and HB-4 boundaries; no operational track record | governance-architect |
Consider Tier 3 (Human-in-Loop) for first 90 days; prioritize decision logging |
| P1 | Decision ledger operational readiness | Ledger initialized but agents not yet configured to write entries | governance-architect |
Verify all agent categories can write to ledger; run first-week logging test |
| P2 | Article gate criterion 5 specificity | “Meets minimum depth” is subjective; no word-count floor or topic-complexity rubric | quality-gate-designer |
Add concrete thresholds referencing BY-OUTPUT-TYPE.md |
| P2 | Code merge compound artifact waiver | Gate requires compound artifact on every PR; one-line typo fixes don’t warrant docs | quality-gate-designer |
Add GM-confirmed waiver for trivial changes (<10 lines, pure bugfix) |
| P2 | Consulting gate criterion 2 refinement | “No unfamiliar tools” may be too strict for client-specific contexts | quality-gate-designer |
Distinguish “haven’t tried” vs “evaluated and rejected”; add client-context exception path |
| P2 | Cross-gate scenario for articles referencing consulting | No defined process for articles needing both editorial + confidentiality gates | quality-gate-designer |
Define cross-gate handoff in INDEX.md |
| P2 | Per-author voice profiles | Rigor test 3 (“sounds like the writer”) is irreducibly subjective | org-genome-builder |
Build first-pass voice profiles per established author for agent approximation |
| P2 | Dual authority matrix maintenance | Genome and governance versions can diverge during learning loop | governance-architect |
Add consistency check to monthly review process |
| P3 | Social gate platform sub-criteria | “Platform appropriateness” is vague for automated assessment | quality-gate-designer |
Add per-platform rules (X: 280 chars, LinkedIn: hook + paragraph structure) |
| P3 | Holdout scenario evolution process | No mechanism for adding holdouts when real failures reveal new modes | quality-gate-designer |
Define quarterly holdout review and expansion process |
| P3 | Anti-pattern specificity improvement | “Consulting from PowerPoint” rated MEDIUM specificity | org-genome-builder |
Add concrete markers: “.pptx-only deliverables with no working demos or exercises” |
| P3 | L2-to-L3 adoption sprint | 7 team members at Level 2 plateau | adoption-sprint-designer |
Design sprint with buddy pairings; focus on tool-building projects |
| P3 | Visibility mechanisms for adoption | Maturity data exists but not systematically shared | adoption-sprint-designer |
Monthly Show-and-Tell, Learnings Feed, Maturity Self-Assessment |
Artifact Inventory
Complete list of governance artifacts checked in this audit:
| Category | Files | Status |
|---|---|---|
| Genome (identity) | MISSION.md, VALUES.md, VOICE.md | Complete, no drafts |
| Genome (decision architecture) | AUTHORITY-MATRIX.md, TRADEOFF-RULES.md | Complete, no drafts |
| Genome (quality standards) | BY-OUTPUT-TYPE.md, ANTI-PATTERNS.md | Complete, no drafts |
| Governance | AUTHORITY-MATRIX.md, HARD-BOUNDARIES.md, ESCALATION-PROTOCOLS.md, POLICY-GENERATION.md, DECISION-LEDGER-SPEC.md, LEARNING-LOOP.md, HUMAN-USAGE-POLICY.md | Complete, no drafts |
| Gates | INDEX.md, article-publication.md, code-merge.md, consulting-deliverable.md, social-media-publication.md | Complete, no drafts |
| Holdouts | 4 holdout files (23 scenarios total) | Complete |
| Operational | AGENT-PRIMER.md | Complete, v1.0 |
| Maturity | maturity-ladder-2026-04-01-1440.md | Complete |
| Audit (coordination) | audit-2026-04-01-1427.md | Complete |
| Decision Ledger | Initialized this audit (see evolution/decision-ledger.md) | NEW |
Total: 21 artifacts across 6 categories. All v1.0. No drafts or placeholders.
Next Review
Date: Monday, 2026-05-04 (first Monday of May) Duration: 60-90 minutes Required attendees: Dan Shipper (CEO), Brandon Gell (CTO) Invited as needed: Kate Lee (if editorial gate data available), Natalia Quintero (if consulting gate data available)
Inputs Needed for May Review
- Decision ledger data – at least 30 days of entries. Verify agents are logging Tier 2+ decisions.
- Gate approval rates – first-pass pass/fail rates per gate if gates are operational.
- Escalation logs – volume, categories, response times, resolution patterns.
- Boundary proximity events – any near-misses on the 9 hard boundaries.
- Plus One operational data – how are subscriber agents performing in client Slack workspaces?
- Adoption sprint results – if the L2-to-L3 sprint has been run by then.
Pre-Meeting Action Items (48h before May review)
- Evolution-auditor generates structured report from decision ledger
- Weekly pattern summaries compiled into monthly view
- Any candidate policies from POLICY-GENERATION pipeline queued for review
Evolution audit #1 complete. Governance v1.0 is structurally sound. No critical gaps. Primary risks: operational readiness (agents need to start logging), Plus One boundary proximity, and the Level 2 adoption plateau. All 21 artifacts are v1.0 with no drafts.
Next audit: 2026-05-04 Governance version: 1.0