From ticket to tested, reviewed release.
A super-intelligent engineering org that works alongside yours. The Engineering SIA plans features, writes and refactors code, generates tests, reviews pull requests, modernizes legacy systems and fixes production issues — in parallel, inside your repositories and your rules.
Plan
Turn a ticket into an explicit, reviewable delivery plan
Test
Keep checks and review in the delivery loop
Parallel
Agents work many tickets at once — not one chat at a time
The software delivery lifecycle: spec, code, test, review, release, and the maintenance nobody wants to do.
The agent roster
Tech Lead
Turns tickets into specs and plans, splits work and assigns it to the right agents.
Builder
Writes and refactors code across the repo, following your conventions and architecture.
Test Engineer
Generates unit, integration and end-to-end tests; hunts flaky tests.
Reviewer
Reviews every PR for correctness, security, performance and style before humans do.
Modernizer
Migrates legacy code — frameworks, languages, cloud — with behavior-preserving tests.
Release Manager
Writes release notes, coordinates deploys and watches the rollout.
Maintainer
Upgrades dependencies, patches CVEs and burns down tech debt on a schedule.
What the Engineering SIA does from day one.
Ticket-to-PR delivery
Assign a Jira or Linear ticket. The SIA writes a plan, implements it on a branch, runs the tests and opens a PR with a clear description and screenshots.
Your backlog moves while you sleep.
Legacy modernization
Map a legacy codebase, generate characterization tests, then migrate module by module — COBOL to Java, AngularJS to React, monolith to services.
Years of migration compressed into quarters.
Test generation & QA
Raise coverage on critical paths, generate end-to-end tests from user stories and quarantine flaky tests automatically.
AI code review
Every PR gets a first review for bugs, security issues, performance and conventions — with suggested fixes humans can accept in one click.
Senior reviewers focus on design.
Incident-to-fix
When production breaks, the SIA reproduces the issue, finds the offending commit, writes the fix and the regression test.
Security & dependency hygiene
Continuous CVE patching, dependency upgrades and secrets scanning, batched into reviewable PRs.
Docs that stay current
API references, architecture docs and onboarding guides regenerated as the code changes.
Embedded & systems engineering
Requirements traceability, test-bench automation and log analysis for firmware, RF and hardware-adjacent teams.
A closed loop, not a one-shot answer.
- 01
Plan
Ticket → spec → task graph, with acceptance criteria.
- 02
Build
Parallel agents implement in isolated branches and sandboxes.
- 03
Verify
Tests, linters, security scans and preview environments.
- 04
Review
Agent review first, then human approval on your rules.
- 05
Ship
Merge, deploy, monitor and write the release notes.
Four modes. One dial per use case.
Observe
Reads signals and builds understanding. No actions.
Recommend
Proposes actions with evidence for a human to take.
Act with approval
Executes once a human approves, by risk tier.
Autonomous
Acts within policy, announces and logs everything.
Autonomy is set per use case and expands only when evaluations against your own historical data prove it is ready.
Guardrails
Governed by default, auditable always.
- Agents work in sandboxes with scoped repo permissions
- Branch protection respected — humans approve every merge
- Secrets never leave your environment
- Every agent change is attributable in git history
Connects to
The systems you already run.
Connections are delivered through Cana MCP adapters. Availability varies by deployment; product names are trademarks of their owners.
What we build the Engineering SIA to deliver.
3×
Throughput per engineer
100%
PRs with a first agent review
0
Merges without human approval
Design goals, not guarantees. Results depend on data, systems and the autonomy level you choose.
Questions, answered.
Which languages and stacks are supported?
All mainstream languages and frameworks. The SIA learns your repo's conventions from the code itself and from the rules files you provide.
How is this different from a coding assistant?
Assistants help one developer in one editor. The Engineering SIA is a coordinated team of agents that plans, builds, tests and reviews in parallel, with governance across the org.
Can we use our own models?
Yes. Route each step to any frontier, open-weight or self-hosted model — or bring your own keys.
Put the Engineering SIA to work. Get early access.
Join the waitlist for early access to Super Intelligent Agents and updates as Cana opens up.