Production & Scale

The complete checklist for taking an agent from prototype to production: infrastructure, monitoring, rollout strategies, and incident response.

Agent Server (formerly LangGraph Platform) is the production runtime for LangGraph agents — with Assistants, Threads, Runs, Cron jobs, and three deployment modes from single host to distributed.

Production-grade error handling: retry strategies, fallback chains, dead letter queues, and graceful degradation patterns.

How to evaluate agent quality: LangSmith datasets, LLM-as-judge scoring, regression testing, and CI/CD integration for agents.

Reducing LLM costs by 60-90%: prompt caching, model tiering, semantic caching, and token budget management.

Architecture for high-scale agent systems: horizontal scaling, queue-based execution, state partitioning, and managing LLM rate limits across a fleet.

Handling concurrent user messages: reject, rollback, interrupt, or enqueue strategies. Built-in LangGraph Platform support.

NeMo Guardrails integration, input/output filtering, PII detection, topic rails, jailbreak prevention, and custom policy enforcement.

Unit testing tools, integration testing full graphs, snapshot testing outputs, mocking LLM responses, and building CI pipelines for agent systems.

Schedule recurring agent runs with cron jobs and receive real-time notifications with webhooks — essential infrastructure for production agent systems.

Run agents or their tools in isolated sandboxes — preventing unauthorized file access, network calls, and credential theft. Providers: Modal, Daytona, Deno, and LangSmith sandboxes.

Updating agents in production without breaking active sessions: graph versioning, state migration strategies, and backward-compatible deploys.

Debug production AI agents systematically: trace analysis through the full pipeline, log correlation from user complaint to specific LLM call, handling common production failures (timeouts, context overflow, tool errors), and structured post-mortems for AI incidents.

Treat prompts as versioned configuration: separate them from code, store in a registry with performance metadata, A/B test changes, and roll back instantly when a prompt change degrades quality.

LangGraph Platform is now LangSmith Deployment — a managed hosting platform for long-running, stateful agents. Cloud, self-hosted, standalone server, and hybrid options.

Running graphs as remote services. Client-server model, SDK integration, authentication, and streaming over HTTP.

Building your own persistence backends. BaseCheckpointSaver interface, custom Store implementations, and migration strategies.

Running LangChain agents on AWS Bedrock: setup, model access, IAM configuration, and production deployment with provisioned throughput.

Choosing between serverless, containerized, and long-running deployment models for AI agents. Load balancing stateful agents, WebSocket vs SSE for streaming, and self-hosted infrastructure patterns.

Managing LLM API rate limits across a fleet of agents: request queuing, token bucket algorithms, graceful degradation, and model fallback chains.

Choosing and configuring storage backends for agent state: PostgreSQL for checkpoints, Redis for short-term state, and the tradeoffs between them.

Reducing LLM API calls through caching: prompt caching, semantic caching, tool result caching, and cache invalidation patterns for agents.

Securing multi-tenant agent systems: user authentication, per-user tool permissions, session isolation, API key management, and tenant-scoped data access.

Defending agents against prompt injection attacks: input sanitization, instruction hierarchy, output validation, and monitoring for exploitation attempts.

Validating what goes into and comes out of your agent: schema validation, PII detection, content filtering, and ensuring agent outputs meet business rules.

Full auth system for Agent Server: @auth.authenticate for identity verification, @auth.on for resource-specific access control, and agent authentication for delegated MCP access.

The full lifecycle of a production knowledge base: ingestion from diverse sources, transformation and chunking, indexing for retrieval, serving under load, incremental refresh strategies, and version management for reproducible agent behavior.

Garbage in, garbage out is amplified with LLMs. Learn to build automated data quality pipelines that detect near-duplicates, track freshness, measure coverage gaps, and score completeness — so your agent never confidently serves stale or incorrect information.

Build closed-loop feedback systems that capture user signals (thumbs up/down, corrections, regenerations), process them into actionable data, and drive measurable improvements to prompts, retrieval, and model selection.

Build a production model registry that tracks which models, prompts, and configs are in use, supports A/B deployment and shadow testing, and enables instant rollback when a new model underperforms.

When and how to self-host LLMs for production: comparing vLLM, TGI, and Ollama, understanding hardware requirements, calculating the break-even point against API costs, and deploying a high-throughput serving stack.

Reduce LLM memory requirements by 2-4x with quantization: understand the tradeoffs between GPTQ, GGUF, and AWQ, measure quality impact at different precision levels, and choose the right approach for your hardware and latency requirements.

Route queries to the right model based on complexity: send simple questions to cheap, fast models and complex reasoning tasks to expensive, capable models. Achieve 40-60% cost reduction with intelligent routing while maintaining quality on hard queries.

Maximize LLM serving throughput with continuous batching, dynamic request grouping, and request coalescing. Understand the prefill vs decode bottleneck and tune the throughput-latency tradeoff for your workload.

Understand the GPU landscape for LLM inference: compare A100, H100, L40S, and A10G on specs and pricing, calculate actual $/token for self-hosted models, model the break-even point against API providers, and optimize with spot instances.