Self-improving agents

Your agent gets better every day it's used.

Every conversation teaches it something. Urai's skill extractor turns those learnings into new Skills automatically, so the agent compounds with use. A fixed set of MCP tools can't: it only improves when someone else ships a better model.

Most agents are frozen the day you ship them

Wire an agent to a fixed list of MCP tools and its ceiling is set. It can't learn a better way to do something it does a hundred times a day.

Capabilities are hand-wired

Every tool is a schema you defined up front. New capability means an engineer writes new plumbing, and the agent never adds it on its own.

No memory of what worked

The clever multi-step solution it found yesterday is gone today. Every session re-derives the same workflow from scratch.

Improvement is rented

The only way it gets smarter is a new model from OpenAI or Anthropic. You wait on someone else's roadmap; your product doesn't compound.

The skill extractor

Conversations become Skills, automatically

A Skill is reusable know-how: the instructions and code for getting one thing done. Urai watches how your agents actually solve problems and writes those Skills for you.

1

Agents do real work

In your app and in the workspace, agents write code and complete workflows across your integrations.

2

The extractor mines them

It reviews successful conversations and spots the repeatable patterns worth keeping.

3

It writes a new Skill

The learning is distilled into a named Skill, instructions plus code, and added to your library.

4

Every agent reuses it

Next time, the agent loads the Skill and does in one step what took exploration the first time.

A flywheel, not a plateau

More usage means more extracted Skills. More Skills mean a more capable agent. A more capable agent gets used more. The loop compounds, and it runs on your data and your workflows, not a model vendor's release schedule.

  • The improvement accrues to you, a moat that grows with every conversation.
  • Skills are versioned and reviewable, so you keep what works and roll back what doesn't.
  • Model upgrades still help; they just aren't the only way forward anymore.
1

Use: agents solve real problems

2

Extract: the skill extractor writes a Skill

3

Reuse: every agent is now better

…and back to the top, compounding each time.

Why an MCP tool server can't do this

MCP tool-calling

  • The toolset is a fixed, hand-authored contract.
  • There's no mechanism to author new capabilities from usage.
  • Yesterday's good solution isn't captured anywhere.
  • Better behaviour waits on the next foundation model.

Urai self-improving agents

  • Skills are written from real conversations, not by hand.
  • The agent gains new capabilities as it's used.
  • Every winning workflow is captured and reused.
  • You compound on your own data, not a vendor's roadmap.

It compounds wherever your agent runs

In-App Assistant

Gets sharper about your product

The assistant embedded in your SaaS learns the flows your users actually take, and turns them into Skills that make the next user's experience faster.

Explore In-App Assistant

AI Workspace

Learns your team's workflows

In the workspace, the agent captures how your team gets work done and hands every project a growing library of ready-made Skills.

Explore AI Workspace

Ship an agent that outgrows the demo.

Book a 30-minute demo. We'll show you the skill extractor turning a real conversation into a reusable Skill.