#Agent Memory

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

8 posts tagged with agent memory. ← Blog home

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Pratik Dhanave · ·5 min read

Memory in Production — Privacy, Forgetting, Isolation, and Proving It Works

A memory system that delights in a demo becomes a liability register in production: it accumulates personal data by design, remembers things users expected to expire, must never leak across tenants, and silently decays in quality unless something measures it. This closing post covers the four disciplines that separate a production memory system from a demo with persistence — privacy, deletion, isolation, and evaluation — and ends where post 1 began: with judgment about what's worth remembering at all.

Production memory is a personal-data system: minimization as selection, answerable 'what do you remember about me', sensitivity tiers, deletion that cascades along provenance edges, hard tenant isolation (memory writes are an injection surface), and layered evals ending in the ablation that proves memory earns its cost. Remembering well is valuable; forgetting well is trustworthy. Closes the series.

#Agent Memory#AI Governance#Privacy#AI Agents#AI Engineering#Evaluation
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
Pratik Dhanave · ·5 min read

Memory as Tools — Paging, Files, and Agents That Manage Their Own Minds

Everything so far treated memory as plumbing the harness operates around a passive model. The most interesting turn in recent agent design flips that: give the model tools to read, write, and reorganize its own memory, and let it decide what's worth keeping. The operating-system analogy arrived early — a small, fast context window backed by a large, slow store, with something paging between them — and it keeps paying dividends. This post covers agentic memory: the OS view, file-based memory, and consolidation.

The agentic turn: context as RAM, store as disk, the model participating in paging policy — plus file-based memory as the transparent, diff-reviewable baseline, and consolidation as the background process that extracts facts, distills lessons, and revises standing memory. The consolidator is the highest-privilege writer in the system; review and evaluate it like one.

#Agent Memory#Agentic Memory#AI Agents#AI Engineering#Evaluation
Pratik Dhanave · ·5 min read

Structured and Graph Memory — When Facts Need Shape

Embeddings are a marvel of forgiving representation: throw anything in, get approximate recall out. But some of what an agent must remember isn't approximate — who reports to whom, which service depends on which, what the user decided and when they reversed it. For facts with structure, similarity search is the wrong instrument, and the fix is as old as databases: give the facts a shape. This post is about entity-centric and graph-shaped memory, and when the extra machinery pays.

Similarity search breaks on aggregation, multi-hop, identity, and what-is-true-now. The fix is shape: entity-centric memory with resolved, superseding claims as the 80% answer; knowledge-graph edges (owns, depends_on, supersedes) where relations are the content; and a hybrid end state where the structured layer answers precisely and the episodic store keeps the receipts.

#Agent Memory#Knowledge Graphs#Entities#AI Agents#AI Engineering
Pratik Dhanave · ·5 min read

Retrieval Memory — Vector Stores, Scoring, and the Art of Remembering the Right Thing

The standard answer to agent memory is "embed it and put it in a vector store" — and it's half right. Semantic search over past experience is genuinely the workhorse of episodic recall. But raw similarity is a weak model of what "relevant memory" means: similar is not important, recent, or true. The difference between a memory system and a junk drawer with an embedding index is the scoring, filtering, and hygiene this post covers.

Memory retrieval is not document RAG: the store is self-generated, time-sensitive, and self-contradicting. Score on relevance x recency x importance, enforce write-side hygiene — self-contained memories, dedupe at the door, attached structure — and inject under a budget with provenance labels so memories are weighed, not absorbed.

#Agent Memory#Vector Search#RAG#AI Agents#AI Engineering#Vector Databases
Pratik Dhanave · ·5 min read

Conversation Memory and Compaction — Surviving the Long Session

The first memory problem every agent hits is the humblest: the conversation itself outgrows the window. Tool outputs pile up, reasoning accumulates, and somewhere past the first hour the agent must start forgetting parts of its own session — the only question is whether it forgets with judgment or at random. Compaction is where memory engineering starts, and it's harder than it looks because the thing doing the summarizing is the thing that needs the summary.

The strategy ladder — buffer, sliding window, selective eviction, compaction — and why the top rung needs engineering: a preservation contract for what survives verbatim, structured rolling summaries, boundary-triggered timing, and the architectural rule that compaction should demote to episodic storage, never delete.

#Agent Memory#Compaction#Context Engineering#AI Agents#AI Engineering
Pratik Dhanave · ·5 min read

The Memory Hierarchy of an Agent

Cognitive science spent a century mapping human memory into distinct systems — working, episodic, semantic, procedural — and agent engineering has been rediscovering the same partition from the opposite direction, because the same pressures produce the same shapes. The hierarchy isn't biomimicry for its own sake: each memory type answers a different question, is stored differently, retrieved differently, and fails differently. Confusing them is the root of most memory-system design mistakes.

Agent memory decomposes into four types with different jobs: working (the context window), episodic (what happened — read as history), semantic (what is true — needs revision, not just insertion), and procedural (how to act — evolves like code). They differ in storage, retrieval, and lifecycle, and most memory bugs are type confusions.

#Agent Memory#AI Agents#Cognitive Architecture#AI Engineering
Pratik Dhanave · ·5 min read

Why Agents Forget — The Context Window Is Not Memory

Every production agent has the same embarrassing failure mode: ask it to continue yesterday's work and it has no idea what you're talking about. The model that reasoned brilliantly for two hours retains nothing of those two hours. This is not a bug to patch but an architecture to build — memory is the system around the model, and designing it well is becoming the distinguishing skill of agent engineering. This series builds agent memory from first principles.

LLMs are stateless, and the context window is working memory — bounded, ephemeral, and unevenly attended — not memory. Forgetting costs re-work, broken user relationships, repeated mistakes, and drift, and no model upgrade fixes it: memory is the system around the model. Opens an 8-post series on building it.

#Agent Memory#AI Agents#Context Engineering#AI Engineering#LLMs

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.