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Describe an agent. Ship it anywhere.

Everything it takes to put an agent to work.

Build it in plain language, connect its tools, decide where people stay in control, and follow every run.

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Build the agent

Start with Agent Builder or a plain-language brief, test the result, and publish only when it is ready.

  1. Build agents with AI
  2. Brief an agent in plain language
  3. Compose work inline
  4. Experiment before release
  5. Release with a rollback path
  6. Score a release against real cases
  7. Catch broken dependencies early
  8. Return data your systems can consume

Choose its intelligence

Match each job to a model on your configured provider account, and keep that choice stable until you change it.

  1. Choose a model that fits the job
  2. Tune models job by job
  3. Run on your provider account
  4. Set an organization model policy
  5. Make model drift visible

Connect its tools

Bring every system the agent needs under one governed tool surface.

  1. Connect the systems your team already uses
  2. Turn your REST API into governed tools
  3. Import tools from an MCP server
  4. Tell safe reads from consequential writes

Put it to work

Start the agent when work arrives, without waiting for someone to open the product.

  1. Run recurring work on the business's clock
  2. Start agents through an API trigger
  3. Use your agents from Claude Code, Cursor and VS Code
  4. Start agents from connected events
  5. Control schedule catch-up

Keep people in control

Choose which actions wait for a person, who makes the decision, and what happens when nobody answers.

  1. Put people in front of risky writes
  2. Review the real action before it runs
  3. Send decisions to the accountable owner
  4. Bound how long work can wait
  5. Never turn silence into consent

Control who can do what

Give each team the access it needs without opening the rest of the workspace.

  1. Let teams own their work
  2. Give people only the actions they need

Ground its answers

Give agents the sources your team trusts, link answers back to them, and choose what happens when they fall short.

  1. Ground agents in maintained sources
  2. Keep answers grounded as knowledge grows
  3. Link answers back to their sources
  4. Choose what happens beyond the corpus

Operate and improve

See what your agents are doing in production, what their answers rested on, and turn what you learn into the next release.

  1. Operate from one monitor room
  2. See which agents are reliable - and which are slow
  3. Spot backlogs before work stalls
  4. Trace every trigger attempt
  5. Bring operating risks to the team
  6. See what every answer rested on
  7. Let it learn from how runs went

Run it reliably

Keep work ordered, recoverable, and visible from the moment Yekar.AI accepts it.

  1. Never lose track of accepted work
  2. Preserve conversational order
  3. Expose uncertain side effects after a crash
  4. Stop work without corrupting its record
  5. Protect downstream systems with a cap
  6. See what drives agent spend

Make actions accountable

Make every external action use the right team, user, or customer account.

  1. Make every external action accountable
  2. Keep connected accounts working
  3. Use one agent safely for many customers
  4. Reduce secret exposure across execution

Protect the workspace

Separate every workspace's data, limit where tools can send it, and protect the credentials that open the door.

  1. Keep one workspace out of another
  2. Contain where a tool can send data
  3. Protect the keys to the workspace

Adapt it to the work

Carry useful context forward, test changes against past work, and handle richer inputs deliberately.

  1. Carry useful context forward
  2. Keep long-running agents within context limits
  3. Test changes against real past cases
  4. Handle images without hidden degradation

Start with the job

See how Yekar.AI would run one of your real processes.

Bring the job, the systems it touches, and the decisions that need a person.

Talk to usRead the docs