Deploying Long-Horizon Agents in Production with Durable Execution and Deepagents Deploy

Learn how to deploy long-running AI agents reliably using purpose-built runtime infrastructure. This guide explains durable execution for resuming agent workflows after failures, checkpoint-based memory for short- and long-term state, human-in-the-loop interruption and resumption, and production-grade observability with tracing and replay. It details how LangSmith Deployment (LSD) and Agent Server provide primitives like task queues, persistence via PostgreSQL, RBAC-based multi-tenancy, middleware guardrails, streaming, and cron scheduling. Discover how deepagents deploy packages these capabilities to eliminate infrastructure overhead and enable scalable, fault-tolerant agent systems.

Continual Learning in AI Agents Happens Across Model, Harness, and Context Layers

Learn how AI agents improve over time by optimizing three distinct layers: model weights, harness infrastructure, and external context/memory. The piece breaks down techniques like SFT and RL for model updates, harness optimization via trace analysis and systems like Meta-Harness, and dynamic context learning through persistent memory, tenant-level configuration, and runtime updates. It highlights practical strategies such as offline evaluation loops, agent trace logging, and 'dreaming' workflows to iteratively refine agent performance without retraining models, emphasizing scalable alternatives to weight updates.

The Agent Improvement Loop with Traces, Evals, and LangSmith

Learn how to systematically improve AI agents using a trace-driven feedback loop powered by LangSmith. The approach centers on collecting execution traces from staging, testing, and production, enriching them with automated evaluations and human annotations, and using those insights to identify failure patterns. Developers then make targeted updates across model prompts, orchestration logic, or context layers, and validate improvements through offline evaluation suites before deployment. Continuous production monitoring with online evals and insights ensures regressions are caught early and performance improves over time. This iterative loop—trace collection, enrichment, debugging, evaluation, and redeployment—enables reliable, data-driven optimization of agent behavior at scale.