Agent Memory

Agent memory is what separates an AI tool that answers each question from scratch from one that learns your business and improves. Without it, you correct the same wrong assumption every week. With it, the correction sticks: the next person who asks gets the right answer because the agent remembers what it was told.

What an agent remembers

Useful memory is typed, not a single bucket of chat history. The categories that matter:

• Corrections — “When you report on slots, always include total_completes.” The highest-confidence memory, because a human said it explicitly.
• Patterns — the agent notices that revenue questions always join fct_invoices to dim_customers, and records it.
• Preferences — “Show me bar charts, not line charts.”
• Context — “The W2 program launched in October 2025,” the kind of fact that changes which rows count.
• Errors — the agent used the wrong date format once, learned the right one, and won’t repeat it.

Retrieval is the easy half

Most “memory” systems are really retrieval: embed past interactions, run semantic search, weight recent entries higher, inject the top hits into the prompt. That part is close to solved. On the standard memory benchmarks, good systems retrieve the right evidence well over 95% of the time, and a strong RAG setup gets you most of the way there.

The trap is assuming retrieval equals correctness. A system can surface the right memory 99% of the time and still answer wrong, because writing the answer is a separate step. We dug into exactly this gap, with reproducible numbers, in our agent-memory benchmark writeup.

Enforcement is the hard half

For an analytics agent writing SQL against your warehouse, remembering a definition isn’t enough. The agent has to use it, and not be overridden by a plausible guess. That means:

• Scope — a memory about one table shouldn’t leak into an unrelated query.
• Authority — a confirmed metric definition outranks a stale guess, every time.
• Blocking — if the agent is about to run SQL that contradicts a known definition, stop it before it touches the database, not after.

This is where memory meets the semantic layer: the definitions you’ve agreed on become rules the agent can’t quietly ignore.

How Datost uses memory

Datost runs an organizational memory that learns from every correction, pattern, and piece of business context, then grounds future answers in it. Its typed layer (Datost Brain) goes past retrieval to enforcement: it scopes memory by org, database, table, and column, ladders authority so a confirmed contract beats a guess, blocks wrong SQL before execution, and audits every retrieval. That’s why accuracy compounds: the more your team corrects and confirms, the harder it is for the agent to be wrong the same way twice. See the benchmarks for how it measures up against the field.