Databricks Agent Failure & Context Management Techniques — E1

How Databricks handles agent memory, sessions, memory pipelines, and retrieval (RAG) — and how to tell which one fixes a given problem.

There are four common problems with AI agents, and each one has a specific fix. Knowing which fix applies to which problem is most of what “context engineering” means in practice.

Sessions apply within one conversation. Memory applies across conversations. A pipeline connects the two. RAG is separate from all three — it’s about grounding answers in real data, not about remembering anything.

MEMORY

Remembering things across conversations

Memory stores let you save a fact once and look it up later, in a different conversation. Here’s the basic pattern:

Important detail: memory search uses BM25 (keyword matching), not vector/semantic search. It will not connect “timezone” to “PST, UTC-8” on its own. The description field is what gets matched against, so write it with the words someone would actually search for.

actor_id should always come from your authenticated app, not from the model or the user's message — otherwise someone could read another person's memories just by asking for a different ID. path is just for organizing entries (like a file path); it doesn't affect search results.

SESSIONS

Keeping one conversation intact

A session stores the turns of a single conversation so they survive things like a server restart.

The store just holds whatever JSON your app appends to it — it doesn’t interpret the contents. That’s why you can use it as a backend for something like the OpenAI Agents SDK or Claude Agent SDK without changing how your agent works.

Sessions and memory are separate . A memory pipeline is what connects the two — it doesn’t replace either one.

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MEMORY PIPELINE

Keeping long conversations from overflowing

In a long conversation, the context window eventually fills up and older messages get dropped. A memory pipeline reads the session, pulls out the facts worth keeping, saves them to the memory store, and trims the session — so the agent doesn’t lose important information just because it ran out of room.

Each run does three things, one actor at a time:

RAG — SEPARATE FROM THE OTHER THREE

Making sure answers are grounded in real data

If an agent states something wrong but sounds confident, that’s not a memory problem — it just doesn’t have the right information in front of it. RAG retrieves relevant content before the agent answers, using AI Search:

RAG fixes hallucination, not forgetting. It works independently of sessions and memory — you can add it without changing either. If the agent is making things up, use RAG. If it’s forgetting things, use memory or sessions instead.

SUMMARY

Problem, cause, and fix

Identifying which of these four problems you’re dealing with is usually enough to know which fix to reach for.

FINAL THOUGHTS

A few things worth keeping in mind

None of these four systems is complicated on its own. The part that actually trips people up is mixing them up — adding a memory store when the real problem is session persistence, or expecting RAG to fix an agent that simply forgot what the user said five minutes ago. Before reaching for any of these, it’s worth spending a minute identifying which of the four problems you actually have.

If you’re studying for the Databricks Context Engineer exam, the four-problem table above is close to the whole test: given a symptom, name the cause, then name the fix. If you’re building this for real, the same table works as a debugging checklist.