Give Autonomous Agents a Memory Store Built for Long-Running Work

In-process variables die when the agent crashes. Vector stores blur the difference between facts and observations. Custom databases require a schema for every new tool the agent learns. The result: autonomous agents that look impressive in a demo and collapse in production.

Six memory types tuned for agent state — Facts the agent verified, events it observed, reflections it generated, skills it learned. Each gets its own typed store with its own retrieval logic.

Conflict resolution between tool outputs — When two tools return contradicting data, MemoryLake flags it and applies your resolution strategy. The agent stops trusting stale facts.

Git-style branches for exploratory runs — Spin up a memory branch for a speculative plan. Merge it if the plan worked. Roll it back if it didn't.

Provenance trail per memory — Every fact links to the tool, document, or conversation that produced it. Critical for debugging and compliance.

1. Connect — Wire MemoryLake into the agent's planning and tool-use loop via SDK or MCP.
2. Structure — Each tool result and reflection gets classified, deduped, and stored.
3. Reuse — Before the next planning step, the agent retrieves the most relevant prior facts, events, and skills.

Teams building autonomous agents for research, operations, browsing, coding, or trading — where the agent runs unattended for long stretches and the cost of restarting from zero is real money.