Agents that see the work through.
Each agent writes a plan you can read, works inside your tools, checks its own result and stops for a sign-off before anything you've marked as risky. Every step lands in a log your team can search.
- of companies fully trust AI agents to run core processes on their own
- 6%
- say they couldn't stop a rogue AI agent right away
- 35%
- integration categories an agent can work in
- 16
Sources: Harvard Business Review Analytic Services, 603 leaders, 2025; WRITER survey, 2,400 respondents, 2026. That trust gap is why every Norkai agent asks before it acts on anything risky.
Six parts, one agent.
Turn on the parts your team needs. Guardrails and traces stay on for every agent.
Planning
Turns a loose request into small steps you can check, and changes course when a step fails.
Memory
Remembers accounts, decisions and preferences from week to week, and you can read or delete any note.
Tools
Reads and writes in the apps you connect, with the same permissions the person asking has.
Guardrails
Spend limits, approval thresholds and blocked actions, set once for the whole workspace.
Checks
Tests each new agent version against real past cases before it touches live work.
Traces
Every plan, tool call, cost and result is recorded, so you can replay any run step by step.
Plan. Act. Check.
Every task follows the same loop, so you always know what an agent did and why.
A plan you can read before it runs
Norkai turns a request into numbered steps and names the tool for each one. Edit the plan, or let it run.
- Steps small enough to check
- A dry run for new workflows
- A new plan when a step fails
Product preview
Refund a duplicate charge for a customer
- 1Confirm the charge is a duplicateBilling
- 2Check the refund policy limitDocs
- 3Ask finance to approve $1,240Chat
- 4Refund and reply to the customerHelpdesk
Real work, in your real tools
Steps run in order, with retries and approvals. Each tool call is logged with its inputs, timing and cost as it happens.
- Automatic retries when a call fails
- A pause for approval above your limits
- A budget cap on every run
Product preview
billing.charges.find(duplicate)212msdocs.get(refund-policy)98mschat.approvals.request()approvedbilling.refunds.create($1,240)running
Waiting on the refund to clear, then replying to the customer.
Checked before it says done
Norkai tests the result against your policies, budget and past cases. Anything that fails goes to a person, with the full context.
- Policy and amount checks
- A score on every run
- Hand-off with a written summary
Product preview
- Refund policy followedPass
- Amount matches the chargePass
- Customer notifiedPass
- Second refund this monthFlagged
How teams use it.
Four runs a team might hand off first. Each one ends with a person deciding.
3 stepsfrom bank feed to a correction
Lyra matches each charge to the ledger, flags the ones that don't fit and drafts the correcting entry. Nothing posts until someone in finance approves it.
3 stepsfrom new ticket to sent reply
Orin tags the ticket, drafts a reply from your help docs and sends it if it's routine. Refunds and upset customers go straight to a person.
4 stepsdone before the morning stand-up
Vega researches each new account, reads its CRM history, drafts a first email and logs it all. The rep reads the draft and decides whether to send it.
4 stepsfrom bug report to pull request
Vega reproduces the bug, writes a failing test, fixes the code and opens a pull request. The code owner reviews and merges.
A fast model sorts and tags. A stronger one plans and writes. Enterprise workspaces can plug in their own model keys instead.
Only inside the limits you set. Anything above a spend threshold, outside an agent's permissions or on your blocked list waits for a person to approve it.
Agents keep short notes on accounts, decisions and preferences in your workspace. You can read, edit or delete any of them, and they never leave the workspace.
The plan, every tool call with its inputs and outputs, how long each step took, what it cost and who approved what. Traces can be searched and exported.
No. Workspace data is only used to do your team's work. Neither Norkai nor its model providers train on it.
Give your team its first agent.
Start with one task your team repeats every week, and see how far an agent can take it.