AI Best Practices

1) Debugging production agent blockers — a systematic checklist

When you see symptoms like “agent looping” or “context drift”, follow this rapid diagnostic flow:

A. Reproduce & Observe

• Capture the full conversation transcript + timestamps + request/response IDs.

• Reproduce with same inputs in a controlled environment (staging) and record all agent steps.

B. Check orchestration / control flow

• Verify orchestrator state machine: are termination conditions implemented? (max_steps, stop_signals, timeouts)

• Look for missing or incorrect done/success signals between agents.

• Ensure message deduplication (idempotency) — looping often caused by re-processing same event.

C. Inspect prompts & tool calls

• Dump the prompt templates and interpolated inputs used for each agent invocation.

• Confirm tool responses are valid and not causing agent to requeue the same task.

D. Trace context propagation

• Confirm context is passed correctly (session id, step id, vector DB pointers).

• Check for context truncation (token limits) or accidental resets.

E. Observe model outputs for hallucination/uncertainty

• Look for repetitious tokens, contradictions, or “I don’t know” loops.

• Check temperature/response randomness settings — high temperature can cause wandering behaviors.

F. Telemetry & Logs to examine

• Per-request logs: prompt, model config (temp, top_p, max_tokens), returned tokens, latency, tool calls.

• Orchestration traces: which agent called which tool and with which outcome.

• Vector DB access: query, similarity score, doc id returned.

• Business metrics: retries, average steps per request, % escalations to human.

G. Quick fixes

• Add step/time limits (max_steps, TTL).

• Add sanity checks: if state unchanged for N steps, escalate/abort.

• Harden stop conditions and guardrails in logic, not only in prompt.

2) Diagnosing context drift (why agent loses track)

Symptoms: agent reference wrong entity, forgets previous facts, or uses stale data.

Causes & checks

• Token truncation: conversation exceeds model context window — check size and implement summarization or RAG.

• Bad retrieval: retrieval returns low-similarity docs; check similarity scores and recall.

• Stateless calls: orchestrator not re-supplying required context (user profile, last actions).

• Inconsistent representation: multiple embeddings or vector DBs with different encoders.

Mitigations

• Use rolling summaries (condense older history into summary + store in memory).

• Store canonical state in a lightweight session DB and pass only needed pieces to model.

• Use retrieval filters (metadata + recency) and require min similarity threshold.

• Detect drift: log semantic similarity between current prompt and last-step context; if below threshold, rehydrate state.

3) Observability & instrumentation (must-haves)

• Structured logs: JSON with fields {request_id, user_id, agent_id, step, prompt_hash, model, temp, embedding_id, similarity_score, latency, outcome}

• Tracing: distributed tracing across orchestrator → model → tool calls (Jaeger/OpenTelemetry).

• Metrics: steps-per-request, avg similarity, avg token usage, failure rate, human escalations.

• Alerts: spike in avg steps, falling similarity, high latency, increased retries.

• Playback & replay: ability to replay any request through staging with identical inputs.

4) AI Engineering Best Practices — teachable checklist

Prompt versioning

• Keep prompts as code artifacts, versioned in Git.

• Tag prompt versions with semantic version (v1.0.0), and include changelog.

• Use feature flags / rollout to compare new prompt versions with a small % of traffic.

Evaluation

• Unit tests for prompts (golden inputs → expected outputs or properties).

• Continuous evaluation: run a battery of tests (accuracy, safety, faithfulness) nightly.

• Use human-in-the-loop evaluations for subjective metrics; maintain labelled corpora.

Retrieval tuning

• Log top-K results + similarity scores.

• Tune top_k, threshold, and re-rankers; consider hybrid search (sparse + dense).

• Use chunking strategy that matches chunk size to model context and semantic unit.

Logging & observability

• Log raw prompts (or hashed), model outputs, embeddings ids, top-K docs, similarity.

• Capture red-team and safety-related flags.

• Ensure PII handling: mask or redact logs per compliance rules.

Testing

• Unit: prompt-level expectations (format, presence of key facts).

• Integration: agent + retrieval + tool interactions.

• Regression: run previous incidents through new model/prompt to prevent recurrence.

• Stress: concurrency and latency under load.

Versioned deployments

• Canary rollout for new models/prompts.

• Shadow testing (run new model in parallel, do not serve output).

• Automated rollback on regressions.

5) Retrieval & vector DB tuning

• Embedding model consistency: Use same encoder for ingestion & queries.

• Chunk size & stride: chunk at semantic boundaries; avoid too large chunks.

• Top-K vs threshold: return top-K then filter by min similarity; fallback to QA chains if below threshold.

• Hybrid search: combine BM25/Elastic with vector search for recall.

• Re-ranker: use lightweight cross-encoder or model-based relevance scoring to re-rank candidates before prompt.

Monitoring: track fraction of queries with similarity < X — set alert.

6) Model-tool fit: how to evaluate & recommend (OpenAI / Anthropic / Gemini / self-hosted)

Use a short evaluation matrix — judge across Capability, Safety, Cost, Latency, Compliance, Fine-tuning/PII control, Ecosystem.

Criteria

• Task type: generation, summarization, code, instruction-following, reasoning.

• Safety & guardrails: built-in safety layers and moderation.

• Latency & throughput: interactive vs bulk jobs.

• Customization: fine-tuning, embeddings, system prompts, RAG support.

• Data residency & compliance: can you keep training data private? enterprise contracts?

• Cost: per-token cost + inference cost at scale.

• Ecosystem & tooling: SDKs, monitoring, prompt tuning tools, connectors.

• Model license & openness: ability to self-host vs API-only.

Example recommendations (rule-of-thumb)

• OpenAI (GPT family) — Great for high-quality generation, broad capabilities, strong ecosystem, but check enterprise data residency & cost. Good for chatbots, summarization, code generation.

• Anthropic — Strong safety-first models (Claude) — good when safety and instruction-following constraints are paramount.

• Gemini — (if available) strong multimodal & reasoning; evaluate latency/cost tradeoffs.

• Self-hosted LLMs (LlamaX, Mistral, etc.) — Good when data residency, fine-tuning on private corpora, or cost control is crucial — but needs infra and MLOps investment.

Decision process

1. Define top-3 business priorities (accuracy, latency, cost).

2. Run a benchmark suite on representative workloads (generation quality, instruction fidelity, hallucination metrics, latency).

3. Evaluate safety/regulatory posture and contract terms.

4. Select candidate(s) and run canary + shadow deploys.

5. Continuously re-evaluate — model landscape evolves fast.

7) Operational patterns to prevent looping & drift

• Deterministic guardrails: max steps, step-idempotency checks, loop counters.

• State diffs: if agent state doesn’t change across steps, abort + escalate.

• Confidence thresholds: if low confidence, escalate to human or fallback.

• Tool-call validation: validate tool outputs and add sanity checks before feeding back to agent.

• Human-in-the-loop gates for high-risk actions.

8) Quick playbook you can teach the team (one-liner checklist)

1. Version prompts & models in Git.

2. Log prompts, model params, tool responses, embeddings, and similarity scores.

3. Canary & shadow new models/prompts.

4. Implement step/time limits and loop detectors.

5. Use hybrid retrieval + re-ranker and monitor similarity distribution.

6. Automate nightly evaluation suites + human review for edge cases.

7. Alert on rising steps-per-request, falling similarity, and escalation rates.