M.I.M.I.R - Multi-agent Intelligent Memory & Insight Repository

# Claudette Suite — Validation, Refinements & Roadmap **Purpose:** Validate the GPT‑5 chat’s Claudette suite proposal, cross‑reference and justify decisions, propose refinements, and surface adjacent chatmodes. This is a concise, actionable report for implementation. --- ## TL;DR - The suite idea is sound. Core gaps (security/auditability, CI artifacts, provenance, token‑constrained variants) are high‑value and correctly prioritized. - Implement the suite as *composable preamble modules* (identity, memoryPolicy, toolPolicy, verification, outputFormat) to avoid combinatorial explosion. - Quick wins: `security-auditor`, `ci-runner`, `provenance`, `architecture-strategist`, `lite-mini`. --- ## 1) Validation of the original chat (what's correct) - Recurring motifs the chat identified are accurate and consistent with good agent design: - Autonomy-first behavioral anchor (agents continue until done) — necessary for agentic workflows. - Role + tone binding ensures predictable style and reasoning approach. - Tool-usage protocols and announce-and-call patterns reduce expensive or unsafe network calls. - Mandatory memory protocol (check/create memory) aligns with reproducibility and long‑term state. - Specialization tiers (Compact, Research, Debug, Ecko) are needed for production/edge tradeoffs. Why these are correct: they map directly to operational concerns (latency, safety, auditability, cost). Enforcing them in preambles reduces variance between agent runs and makes orchestration deterministic. --- ## 2) Cross-reference & justification for the chat’s prioritization - Security auditor first: justified. Security/PII leaks in agentic systems are high‑impact, high‑cost. Adding default safe behavior mitigates broad risk. - CI runner second: practical ROI — integrates easily into CI pipelines producing machine‑readable artifacts (JUnit/JSON) and reduces human overhead. - Provenance third: enterprise customers and legal contexts require claim provenance — adding structured provenance increases trust and auditability. Concrete mapping to repository needs: - Preambles should align with the repository’s existing agent flavors (Auto, Debug, Ecko). Where `claudette-auto` exists, add a `security` module that can be composed into it. - The `ci-runner` maps to existing Debug/Test tooling (extend `Debug` + `CI-output` module). Justification summary: the prioritized items address risk (security), operational automation (CI), and compliance (provenance) — the classic risk/benefit trifecta that accelerates adoption. --- ## 3) Refinements & design decisions (my recommendations) 1. Preamble composability (Preamble Factory) - Split preambles into modules: `identity`, `memory_policy`, `tool_policy`, `verification`, `output_format`, `autonomy_level`, `telemetry`. - Implement a small composer (script or simple CLI) that concatenates chosen modules into a final preamble file and emits a metadata manifest. 2. Agent manifest & metadata header (canonical) - Each agent run MUST end with a JSON metadata header (machine‑parsable). Example: ```json {"agent":"claudette-security-auditor","version":"v1.0","run_id":"<uuid>","start":"<iso>","end":"<iso>","memory_policy":"no_persist","tools":["fs","lint"],"verification_level":"strict","autonomy":"semi-autonomous"} ``` - Use this manifest for orchestration, search, audits, and routing. 3. Provenance HMAC header - For high‑trust outputs include a provenance header HMACed with a signing key held by the orchestration layer. This enables tamper evidence for legal/audit uses. 4. Emergency-stop & telemetry clamps - Every higher‑autonomy preamble should include: `ESCALATE:STOP` rule; numeric network_call limits; max tool_calls threshold; and a telemetry report (toolCalls, netCalls, diskWrites). 5. Memory tagging & encryption - Any memory writes that may contain sensitive data must be labelled (e.g., `SEC:REDACTED`) and encrypted at rest. The Security Auditor preamble should require this by default. 6. Output formalism options - Provide three output formats as modules: `free_text`, `structured_json`, `junit_xml`. Orchestration can require structured formats for CI/automation. --- ## 4) Adjacent chatmodes (ideas to add) — concise list These expand practical uses and are simple to implement as modules or small preambles. - On‑call Responder: triage runbooks, escalate, produce playbook steps, require explicit human approval before write actions. - Experiment Runner: manage A/B tests, track metrics, produce reproducible experiment manifests. - Compliance Lawyer: focus on legal phrasing, retention policy checks, GDPR/CCPA flags, and produce redaction suggestions. - Whiteboard Brainstorm: iterative idea generation with constraints (no tooling, purely ideation). - Customer Persona Roleplayer: simulate stakeholder feedback (product/design validation). - Data‑Engineer Orchestrator: ETL-focused, enforces schema checks and sample data outputs. - Summarizer Aggregator: long-context chunking + provenance + stitched summary for legal/archival usage. Each of these is a small module away from the recommended suite and can be composed with memory/verification modules. --- ## 5) Example preambles worth shipping first (ordered) 1. `claudette-security-auditor` — high value for safety and adoption. 2. `claudette-ci-runner` — practical automation; produce JUnit/JSON artifacts. 3. `claudette-provenance` — for legal/compliance outputs and research. 4. `claudette-architecture-strategist` — product/technical decision documents. 5. `claudette-lite-mini` — cost-sensitive, low‑token footprint variant. Implementation note: prefer composing `security` + `auto` for the first, `debug` + `junit` for CI runner, and `research` + `provenance` for provenance agent. --- ## 6) Minimal manifest & metadata spec (proposal) - `preamble.yaml` (human) / `preamble.json` (machine) fields: - `name`, `version`, `modules`: [identity,memory_policy,tool_policy,verification,output_format], `autonomy_level`, `verification_level`, `signed` (bool). Example JSON manifest (small): ```json { "name":"claudette-ci-runner", "version":"v1.0", "modules":["identity:ci-runner","memory:ephemeral","tool:runCommands","output:junit_xml"], "autonomy":"semi-autonomous", "verification":"basic", "signed":false } ``` --- ## 7) Implementation roadmap (practical steps & quick wins — 2 week sprint) 1. Week 0 — scaffolding - Create `docs/agents/modules/` and short README describing module format. - Add `compose.js` (or node script) to assemble preambles and emit manifest. 2. Week 1 — ship quick wins - Implement `security-auditor.md` (module + full preamble). - Implement `ci-runner.md` (preamble + JUnit example). - Add metadata header enforcement in orchestration layer (small change in `task-executor`/`task-executor.ts` to capture metadata if present). 3. Week 2 — provenance + tests - Implement `provenance` module (HMAC header pattern) and add to `research` preamble. - Add test harness using existing `Debug` preamble to validate each new preamble and produce sample artifacts. Deliverables: 5 MD preambles, composer script, manifest JSON schema, small test harness that runs each preamble once and validates output metadata. --- ## 8) Small technical caveats & choices to make - Where to store signing keys for provenance? Recommendation: orchestration host keystore (not persistent agent memory). Use short‑lived signing keys and rotate. - Encryption for memory: use repository key management (KMS) or local disk encryption; start with `AES-256` with keys in orchestration layer. - Telemetry: aggregate tool call counts and net calls in metadata; be conservative about PII in telemetry payloads. --- ## 9) Example short templates (one-line examples) - Metadata header (agent end-of-run JSON): ```json {"agent":"claudette-ci-runner","run_id":"uuid","start":"iso","end":"iso","memory":"ephemeral","tools":["runCommands"],"verification":"basic"} ``` - Emergency-stop rule (preamble sentence): > If you output the token `ESCALATE:STOP` or exceed `network_calls > 10` or `tool_calls > 200`, immediately stop and emit full telemetry and a human escalation record. --- ## 10) Conclusion & next step I will take (if you want me to continue) - The ideas in the GPT‑5 chat are validated and well‑prioritized. Composability, a small manifest scheme, and an emergency-stop/telemetry policy are the highest‑leverage refinements. Next step I can do for you (pick one): - Generate full MD files for the top 5 preambles (production-ready preambles you can paste into your gist), or - Produce a small `compose.js` script (Node) and example module files so you can build preambles programmatically. --- *File created:* `docs/agents/CLAUDETTE_SUITE.md` — implement and iterate as needed.