Agents that ship code that runs — not drafts you have to fix.
Before an agent writes anything, it states back what it understood and waits for your confirmation. Then it builds, deploys its own work to a sandbox, runs it, and fixes what breaks — raising a PR only once it passes. You review validated diffs, never first drafts.
Add claims dashboard from policy data
nectorq/claims-dashboard → main · reviewed diff
- Sandbox runpassed
- Testspassed
- Security scanclean
- Buildpassed
You've tried AI agents. You got confident code that solved the wrong problem.
It never asked a question.
An ambiguous request taken at face value, and something fast and wrong built on top of it.
It compiles in the demo, breaks in your repo.
Output that looks right and fails on contact with real code. Your engineers spend the saved time debugging it.
It forgets everything by Monday.
Every session starts with the same re-briefing.
The agent tells you what it heard. You decide if it's right.
Ambiguity is where agent work goes wrong, and it goes wrong quietly. Nectorq agents resolve it first — questions on anything underspecified, then a written summary of the requirement as understood: data model, interfaces, constraints, edge cases. Nothing is built until you confirm that summary. Correcting a misunderstanding here costs a click. Correcting it in review costs a sprint.
- Questions before code
- A written understanding, not your request read back to you
- Work starts on your confirmation, not before
Your request
“Build a claims dashboard from our policy data.”
Here’s what I understood
- Data model
- Policy, Claim, Adjuster — claims 1:many to policy
- Interfaces
- REST API · 4 read endpoints
- Constraints
- Read-only warehouse · PII masked before any model call
- Edge cases
- Partial claims, multi-currency, reopened claims
Correct anything here before work starts — a click now, not a sprint in review.
The agent proves its work before you ever see it.
Once the brief is confirmed, agents don't stop at generating code. Every task runs a closed loop — build, deploy to an ephemeral sandbox, execute, catch what fails, correct it — and repeats until it passes. Only then does a PR reach your queue.
↺ Validate loops back to Build until it passes, before any PR
No PR reaches you until it has run and passed in a sandbox. The full run — what failed, what the agent changed, what finally passed — is in the agent audit log.
One conductor. Many specialists.
Work isn't handed to one general-purpose agent. A conductor decomposes the confirmed brief and dispatches it to agents specialized by capability — planning, building, verifying — running in parallel rather than turn by turn, each with bounded autonomy at its step.
Expert agents, built for the work they do.
The fleet is specialized twice over — by capability (planning, building, verifying) and by domain. The agents that build a data pipeline aren't the ones that answer a question or ship a service; each is built for its craft.
Data engineering
Source profiling, schema discovery, pipeline and transformation logic, and quality checks — deployed across your warehouses and multi-cloud, not hand-plumbed.
Converse
Plain-language questions mapped to your schema, the query written and validated, and a knowledge base that compounds with every answer.
Software engineering
APIs, services, and full-stack applications — designed, built, tested, and security-scanned end to end, not scaffolded and left for you.
Low spend and full context — without you managing either.
Automatic model routing
Every task is classified at dispatch: Strategic, Build, or Commodity. Frontier accuracy where it counts, fast and lean where it doesn't. No manual configuration, and no frontier prices for boilerplate.
Semantic cache
Repeated and equivalent work is served from cache and never sent to a model at all. Spend falls in proportion to how much your work repeats.
Persistent memory
Agents remember every file, decision and constraint, permanently. No session boundary. You never brief the same background twice.
Validated work. Lower spend. No re-explaining.
Fewer review cycles
You review diffs that already ran and passed.
No wrong-problem rework
Misunderstandings surface before the build, not after.
No silent drift
A brief that stops fitting reality gets flagged, never worked around.
Lower token cost
Routing and cache cut spend without touching quality.
Consistent output
Memory keeps new work aligned with everything built before it.
See it run on your stack.
A scoped pilot on a real repository, inside your perimeter. You'll see the confirmed brief, the sandbox run, and the PR that came out of it.