Production Best Practices & Guidelines

A consolidated reference for teams moving agentic AI systems from prototype to production. Each page covers a specific production concern with a structured best practices table: Key Challenge | Description | Lessons Learned & Alternatives Considered | Solution Applied.

Pages in This Section

Page	What It Covers
Observability	Tracing, metrics, logs, evaluations, cost visibility, tooling (Langfuse, LangSmith, Braintrust)
State & Memory Management	Memory tiers, LTM strategies, session persistence, memory solutions (Mem0, Zep, AgentFS)
Deployment	GenOps, canary rollouts, prompt versioning, durable execution, multi-agent coordination
Agent Testing & Evaluations	LLM-as-judge, eval frameworks (DeepEval, RAGAS), benchmarks (GAIA, SWE-Bench), platforms
Context Engineering	Context rot, compaction, retrieval, isolation, caching — with references from Manus, Anthropic, LangGraph
Agent Security	Prompt injection, least privilege, HITL, audit trails, NIST AI RMF, Google SAIF, AWS Scoping Matrix
Cost Management	Model routing, prompt caching, token budgets, cost monitoring, vendor-specific guidance

Vendor Best Practices

Industry guidance from the major AI providers on building production-ready agents:

Vendor	Key Resources
Anthropic	Building Effective Agents · Context Engineering for AI Agents · Long-Running Agent Harnesses · Claude Code Best Practices
Google	Introduction to Agents · Agent Tools & MCP Interoperability · Context Engineering: Sessions & Memory · Agent Quality · Prototype to Production
Microsoft	AI Agents for Beginners – 12 Lessons · Microsoft Agent Framework · AutoGen Docs · Semantic Kernel
OpenAI	A Practical Guide to Building Agents · OpenAI Cookbook · Safety Best Practices

Universal Production Principles

Principle	Rationale
Instrument before you deploy	Retrofitting observability is significantly harder than building it in from the start
Treat prompts as versioned artifacts	Prompt changes are as impactful as code changes — track, review, and roll back like code
Apply least privilege to tool access	Agents with broad permissions cause broader blast radius when they go wrong
Set cost alerts from day one	Token costs compound silently; catching overruns on the monthly bill is too late
Test continuously in production	One-time pre-deployment evaluation misses quality drift over time
Design for graceful degradation	Agents should fail safely — define fallback behaviors and HITL escalation paths
Enforce tenant isolation everywhere	Memory, caches, and tool configs must be strictly namespaced in multi-tenant deployments