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Explore our latest articles and insights about AI Agents.
86 posts in total
Which Python dependency manager should you use for production agent services in 2026? The install speed, lockfile story, and Docker build times compared.
How to build retry logic that handles rate limits, timeouts, and transient failures without burning money. The backoff rules and the 3 errors you must not retry.
How to use Pydantic models to force your RAG planner LLM to return structured steps. The schema, the retry loop, and why plain JSON prompts break in production.
How LLM-powered query rewriting fixes vague user questions before retrieval. The prompt, the multi-query fan-out, and when rewriting hurts more than helps.
How to debug a live agent incident using Langfuse traces. The search patterns, the 5-minute workflow, and the post-mortem that catches the root cause.
How to use Langfuse trace data to find where your agent burns tokens. The 4 queries, the cost-per-user view, and the 50 percent savings patterns.
How to combine Langfuse traces with Grafana dashboards for agent monitoring. The integration, the panels, and the alerting that catches real problems.
How Prometheus metrics surface performance bottlenecks in agentic AI. The 3 queries, the alert rules, and the dashboard that finds hot loops fast.
How to stress test an agentic AI service before it ships. Concurrency, tokens, latency budgets, and the load profile that simulates real traffic.
How to wire eval pipelines into CI so every agent change is scored automatically. The nightly job, the regression gate, and the dashboard that matters.
How to load evaluation metrics dynamically in a Python eval pipeline. The registry pattern, entry points, and the test override that makes CI fast.
Why LLM judges without explicit reasoning drift, and how chain-of-thought rationales make their scores defensible. The prompt, the parser, the trust.