Azquo for AI Agents
A persistent, auditable memory layer for AI agents — built, queried, and reorganised by the agents themselves.
The missing layer
AI agents are good at reading files, writing queries, and explaining results. What they lack is somewhere trustworthy to put what they learn. A transactional database demands a schema up front; a spreadsheet has no audit trail; a context window forgets everything when the session ends.
Azquo is that missing layer: a persistent, auditable store that an agent can build, query, and reorganise on its own — and that remembers every decision the agent made.
What an agent can do through the Azquo MCP server
The Azquo MCP server gives any MCP-capable AI agent direct tools to: create databases; import raw files; design and refine import templates; query with AQL; browse and extend hierarchies; build and run reports; and audit any individual figure back through every transformation to its source.
Proven, not promised
In a recent demonstration, Claude built a complete insurance loss-cost database from a 168-page ISO Commercial Auto filing covering 73 territories — unaided. It designed the import templates, structured the hierarchies, and produced the reports, with every step recorded in the database itself.
Every value in the rating chain — not just the total — traces back to the file the agent imported, the user who ran the import, and the date. Click any cell.
Institutional memory for agents
Every quirk uncovered, every convention agreed, every interpretive decision an agent makes is stored as part of the database — anchored to your business and available to the next agent that connects. Agents stop starting from zero.
Two-way with your spreadsheets
The same store speaks Excel directly. Analysts pull live data in, write changes back, and share with colleagues. The AI layer and the spreadsheet layer see one source of truth, and every change — from either side — is on the record.
Why the architecture matters
Azquo stores names in hierarchies, not rows in tables. There is no schema to design before data arrives; any data can be loaded at any time, and new groupings or classifications can be added later without touching existing data or breaking existing reports. This flexibility is precisely what agent-driven work needs: the structure can grow as understanding grows.
Read more: How Azquo Thinks
Try it
The MCP runs on our infrastructure at mcp.azquo.com. Evaluation access is granted on request and includes a pre-configured Claude Code snippet, your own Azquo account, and a credential pair tied to your user — so every agent action is attributed back to you in the audit trail.
If you are building agent systems and want a memory and audit layer underneath them, we would like to talk.