Stage 06 of 6 · The Closed Loop
Evaluate — Trace every span, score every output, feed corrections back into the loop.
Stage 06 of Implement Agentic's six-stage closed loop. Below: process, technologies, ecosystem partners, build-vs-buy guidance, project lifecycle and the benefits clients see.
Process
- Step 01
Maintain a golden test set of 30–100 representative tasks per agent.
- Step 02
Use LLM-as-judge scorers for subjective dimensions (tone, completeness, citation grounding) plus deterministic scorers for structured output.
- Step 03
Block deployments on regression with CI/CD eval gates.
- Step 04
Sample production traces into the eval set so it grows with reality.
- Step 05
Feed human corrections back as DPO/LoRA fine-tunes, Skills updates, memory writes or new test cases.
Technologies
The 2026 production stack for this stage. Implement Agentic is deliberately model- and vendor-neutral.
Observability & eval
- Langfuse (open source)
- LangSmith
- Braintrust
- Arize Phoenix / AX
- Helicone
- Latitude
Fine-tune / preference
- DPO
- LoRA
- Anthropic Skills
- OpenAI fine-tune
Ecosystem partners
Langfuse
Self-hostable trace + eval store (default for regulated client work)
Braintrust
Eval-first CI/CD blocking; 1M free spans/mo
LangSmith
Deepest LangGraph integration when already on LangChain
See the full Ecosystem partners page for our tool catalog.
Build-vs-buy decisions
Buy when…
Buy the eval/observability platform — these are mature, undifferentiated and integrate via OpenTelemetry.
Build when…
Build the golden test sets, scorers and feedback consolidation — they encode what 'good' means for your business.
Implement Agentic's recommendation
Langfuse self-hosted for regulated clients; Braintrust for fastest CI integration; LangSmith if already on LangChain.
Project lifecycle
| Phase | Timing | Outcome |
|---|---|---|
| Golden set | Week 1 | 30–100 tasks defined with expected outputs and scorers. |
| Tracing wired | Week 2 | Every span captured; PII redacted at the gateway. |
| CI gates | Week 3 | Deploys blocked on regression; nightly eval runs in place. |
| Operate | Ongoing | Production failures auto-promoted into the eval set; weekly reviews. |
Benefits
- Regression-proof deploys — no silent quality drift on prompt or model changes.
- Continuous improvement: every human correction becomes a future test case.
- Closed loop actually closes — stage 06 feeds stages 03 (memory) and 04 (reason).
Failure modes we prevent
- No golden test set — quality changes are invisible until customers complain.
- Treating evals as a one-time launch checklist instead of a CI gate.
- Discarding human corrections instead of routing them into the next training/eval cycle.