Live Status
GESA Implementation Status — v0.2
What is live in production, what is specified, and what is on the roadmap.
Full Status Map
| Component | Status | Implementation |
|---|---|---|
| Conceptual framework | ✅ Complete | GESA v0.2 specification |
| Episode schema | ✅ Live | D1 orchestrator_processing_log |
| Episode capture (AOP) | ✅ Live | WorkflowLogger, non-blocking, 12 steps |
| Active episode buffer | ✅ Live | KV EXECUTION_LOGS, 7-day TTL |
| Long-term episode store | ✅ Live | D1 persistent database |
| Temporal decay | ✅ Live | KV TTL + D1 retention policy |
| DRIFT measurement | ✅ Live | Quality Score System (85–100 scale) |
| Gap velocity tracking | ✅ Live | Quality Analytics (monthly trends) |
| 6D dimensional agents | ✅ Live | 8-agent orchestrator (per-dimension) |
| Temperature schedule | ✅ Live | 4-tier depth system (512→2048 tokens) |
| Synthesis / GENERATE step | ✅ Live | Synthesis Engine (cross-agent patterns) |
| Semantic Intent / observable properties | ✅ Live | Semantic Anchoring pattern |
| Temperature profiles (spec) | ✅ Specified | Fast / Standard / Slow / Adaptive defined |
| Similarity function | ✅ Specified | 5-weight composite function documented |
| Cold start handling | ✅ Defined | 20-episode threshold, T₀ fallback |
| Observable output schema | ✅ Complete | GESARecommendation interface |
| Episode retrieval layer | 📋 Roadmap | "Identify patterns in failures" |
| Strategy generation layer | 📋 Roadmap | "ML Quality Prediction" |
| Adaptive cooling (full) | 📋 Roadmap | "Adaptive scoring algorithms" |
| Candidate selection layer | 📋 Roadmap | "Client preference learning" |
| HEAT integration | 📋 Specified, not implemented | Full spec documented |
| Generator interface | ⚙️ Domain-specific | Rule / LLM / hybrid — TBD per domain |
What "Live" Means
The ✅ Live items above run inside the StratIQX AI consulting platform in production. StratIQX was the first system to implement GESA — before the pattern was named.
The platform processes enterprise consulting reports through:
- 11 AI model orchestrations in sequence and parallel
- Cloudflare edge infrastructure (Workers, D1, KV, R2, Queues)
- Azure-hosted Claude pipeline → LaTeX → PDF output
- WorkflowLogger capturing every boundary as a non-blocking episode
The episode store is populated. The DRIFT measurement layer is running. The temperature schedule (depth tiers) is live. The synthesis/generation step is live.
The gap between "Live" and "Roadmap" is the retrieval layer — the step that reads back from the episode history and closes the learning loop. Everything needed to build that layer is already in place.
The Roadmap Gap
Four independent internal documents each describe the same missing layer without naming it:
| Document | Future Enhancement Language | GESA Step |
|---|---|---|
| Execution Logging | "Identify patterns in failures" | RETRIEVE |
| Workflow Model | "Failure Pattern Analysis" | RETRIEVE |
| Quality Score | "ML Quality Prediction" | GENERATE |
| Quality Score | "Adaptive scoring algorithms" | ANNEAL |
| Quality Score | "Client preference learning" | SELECT |
The system has been writing its own next chapter. GESA is the architecture that names and formalises what the roadmap is reaching toward.
Specification Version
| Field | Value |
|---|---|
| Version | v0.2 |
| Status | High-Level Specification |
| License | Creative Commons Attribution |
| Companion frameworks | cormorantforaging.dev · fetch.cormorantforaging.dev · drift.cormorantforaging.dev · stratiqx.com |
Last updated: February 2026