#Procedural Memory

Articles about Procedural Memory — exploring patterns, best practices, and real-world implementations in production systems.

1 post tagged with procedural memory. ← Blog home

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Pratik Dhanave · ·5 min read

Learning from Experience — Procedural Memory and Agents That Get Better

Everything before this post helps an agent remember; this one asks whether it can improve. The difference is procedural: an agent that recalls last week's failed deploy has memory, while an agent that deploys differently because of it has learned. Procedural learning is the highest-leverage loop in agent engineering — every improvement compounds across all future tasks — and the most dangerous, for exactly the same reason. The craft is building the loop with brakes.

Learning without weight changes lands in artifacts: rules, playbooks, worked examples, failure memory. The loop breaks at misattribution, overgeneralization, rot, and conflict — and procedural writes deserve deploy-grade brakes: proposal review, provenance, blast-radius scoping, regression evals. Start with failure memory and worked examples; they compound at a fraction of the risk.

#Agent Memory#Procedural Memory#Self-Improving Agents#AI Agents#AI Engineering#Evaluation

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse the blog or explore related topics using the tag cloud above.