The Hard Part Isn't Building Agents—It's
Everything After

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.

LangSmith introduces reusable evaluators and a library of 30+ evaluator templates to standardize and scale agent evaluation across projects. Teams can define evaluation logic once and apply it across tracing workflows, ensuring consistent safety checks, response quality metrics, and trajectory validation. The templates cover safety (prompt injection, PII, toxicity), response quality, multi-step agent trajectories, user behavior analysis, and multimodal outputs. These evaluators support both online monitoring of production traffic and offline experimentation, enabling teams to detect failures, analyze agent decisions, and continuously improve performance without rebuilding evaluation logic from scratch.

Use evaluation-driven feedback loops to iteratively improve agent harnesses and achieve better generalization in production. Better-Harness treats evals as training data for agents, where each test case provides a learning signal to optimize prompts, tools, and workflows. The system combines curated eval sourcing (hand-written cases, production traces, external datasets), structured tagging for behavioral coverage, and holdout sets to prevent overfitting. It introduces a compound system approach—data sourcing, experiment design, optimization, and human review—to continuously refine agent performance. Key practices include mining production traces for failures, using tagged eval subsets for cost-efficient testing, and pairing automated improvements with human validation to avoid reward hacking and ensure real-world reliability.

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.

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.

Build effective agent evaluation systems by starting with simple, high-signal end-to-end evals and iteratively increasing complexity. Use observability tools like LangSmith to analyze real agent traces, define clear success criteria, and separate capability vs regression evals. Focus heavily on failure analysis by categorizing issues (prompt design, tool interfaces, model limits, or data gaps) before automating evaluation. Leverage evaluation levels—single-step (run), full-turn (trace), and multi-turn (thread)—with trace-level evals as the most practical starting point. Ensure infrastructure issues are ruled out, assign ownership to a domain expert, and validate not just outputs but real-world state changes. This approach improves agent reliability, debugging, and continuous performance optimization.

Learn how to build targeted evaluation systems that directly shape agent behavior by measuring real-world capabilities like tool use, retrieval, and multi-step reasoning. This approach emphasizes curating evals from production traces, dogfooding feedback, and adapted benchmarks, rather than relying on large generic test suites. The system uses categorized evals (e.g., tool_use, memory, retrieval) and metrics such as correctness, step ratio, tool call ratio, latency ratio, and solve rate to assess both accuracy and efficiency. By analyzing traces and defining ideal execution trajectories, teams can iteratively improve agent performance, reduce cost, and prevent regressions while maintaining alignment with production needs.

Deep Agents introduces a tool in its Python SDK and CLI that allows agents to autonomously compress their context windows at optimal moments. Instead of relying on fixed token thresholds, agents can now summarize older context when it becomes less relevant—such as at task boundaries, before large context ingestion, or after extracting key insights. This improves efficiency, reduces context rot, and aligns memory management with the agent’s reasoning process. The system retains recent messages (about 10% of context) while summarizing older interactions, enabling better long-horizon performance without manual intervention or rigid harness tuning.