Agents that learn between runs.

The difference between an agent that improves and an agent that repeats itself is memory. A coding agent that rediscovers your architecture every morning, a research agent that re-asks the same clarifying questions, a scheduled job that forgets last week's conclusions — all of them are missing the same primitive: a durable store that outlives the process.

VeelIQ provides that store as a hosted MCP service. Any agent that speaks MCP — Claude Code, agents built on the Claude API, or custom runtimes — can persist facts during a run and load the relevant ones at the start of the next.

Problem

• Agent state dies with the process; every run starts cold.
• Context windows can't help across runs — they are per-conversation by definition.
• Rolling your own memory means standing up a database, embeddings, and retrieval logic per project.
• Homegrown memory is usually unauditable: nobody knows what the agent has stored.

Solution

Point the agent at your VeelIQ MCP endpoint. Its loop gains three durable operations:

• Write — remember persists decisions and discoveries as they happen ("migration blocked by legacy auth service — workaround documented in notes.pdf").
• Read — recall at run start loads the facts relevant to today's task, by semantic similarity.
• Ground — search_knowledge pulls supporting detail from project documents on demand.

Memory hygiene matters for agents even more than for chat — see the architecture guide for write policies and retrieval patterns.

Benefits

• Cumulative progress: each run builds on the last instead of rediscovering it.
• Fewer interruptions: the agent stops re-asking answered questions.
• Shared state: multiple agents (or an agent and its human) read the same memory.
• Auditability: every fact the agent stored is visible and deletable in the dashboard.
• Zero memory infrastructure to build or operate.

Features used

Memory and knowledge search from Context, plus the instructions library — agents can load standing procedures ("deploy checklist") the same way they load facts.

Example

A nightly release-notes agent runs at 02:00. On each run it recalls the style decisions and product naming saved from previous runs, searches the changelog PDFs uploaded to knowledge, drafts the notes, and remembers anything new the maintainer corrected. By week three, corrections have dropped to near zero — the agent has effectively been trained by its own memory, without touching a model.

FAQ

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