Linear or JiraBuild new. AI-native from the first commit. The Knowledge base grows with the product, so maintaining and scaling it later never becomes a headache.
Coding agents with human supervision
Your backlog gets moving again, and your team stays in control.
Coding agents build the product still stuck on paper and modernize the system nobody wants to touch. Your team decides what goes through approval, and every session is logged, from request to deploy.
The animation shows a ship drawn in characters, scanned from the old hull, rebuilt from the keel up until it becomes a photograph of the same ship. Every piece of the material changes; the silhouette never does.
Ship of Theseus paradoxIf your software is refactored, is it still the same software?
The cost today
Building from scratch is slow. Touching legacy is frightening.
Traditional development takes too long. And touching a system that is already running is a risk nobody wants to take.
Log · what stalls todayLive
Build from scratch
001More people on the team do not mean more shipped, it means more to manage.
002Documentation falls behind as soon as the project moves.
003Every fix and every improvement takes forever.
Refactor
004The people who wrote the system have left, and the documentation does not help.
005Nobody wants to risk breaking what is running.
006Every incident pulls the whole team off building to fight the fire.
▸
The platform
How the product is built and sustained.
Operated by AI · supervised by humansRunning
In: build new. In: refactor legacy. Both run on the same platform. Stage 01 Knowledge base: codebase, docs, anonymized data (indexing). Stage 02 build / refactor: new, born AI-native; legacy, modern, typed, AI-native (building). Stage 03 observability: instrumented end to end (live). Stage 04 sustainment: tickets in, fixes out (human supervising).
InBuild newRefactor legacy↳Both run on the same platform
The agent flow
From request to production.
Requests arrive from any channel and run through to production. Every run leaves the flow a little better.
The agent flow running in production at Ithaca, from request to deploy. Requests arrive through Slack, Microsoft Teams, Zoom, Google Meet, Granola, Fireflies, comments left on the interface itself and GitHub Actions. Four agents watch those channels: the channel sweep, every hour; the meeting sweep, every 3 hours; direct asks, by mention, DM or webhook; and CI triage, every 4 hours on weekdays. In an isolated run, triage removes duplicates and sizes each ask: small ones go to build, the large ones go to the team to scope, and the ones that only need following become an issue in Linear or Jira. The build runs in a sandbox on a clean clone, reproduces the problem, applies the smallest fix, runs tests and lint and records before-and-after evidence. The pull request goes through the automated reviewer on every push and the PR companion, with optional human review by your team. Then come the merge queue, promotion from main, post-deploy QA, release notes and the daily digest, and the merge goes back as a note in the thread it came from. Underneath it all, observability reviews every run, finds where the flow stalled or went wrong and sends the adjustment back to the step that needs it: for example, when the same request arrives from a meeting and from a thread, the meeting sweep starts checking the channels before opening a request.
Record
Every step is visible, from request to response.
Observability · the product screen56s
The video walks the platform’s observability screen: the run queue, one session opened step by step, a step’s request and response, and the trail that links them. Each step carries its duration, its cost and the model that ran it. There is no audio.
What this screen records, field by field: observability for coding agents.
Challenges
A new project or an untouchable system: the same platform.
One is building what does not exist yet. The other is changing what cannot go offline.
Refactor legacy. All the existing code and documentation are read before a single new line is written, so the business rules survive. The system is rebuilt on a modern architecture, tested, and only then replaces the old one.
AI-native by construction · operated by AI · supervised by your team
Two parts of this argument have pages of their own: why the platform is not a dark factory and how legacy is modernized without stopping the business.
What each session has to record is in observability for coding agents.
Who answers for what the AI wrote is in software development with AI, under human supervision.
What AI-native SDLC, agentic SDLC and AWS AI-DLC each mean is in AI-native SDLC, agentic SDLC and AWS AI-DLC: what each term means.
Cases
Proven results.
Deployment 01
Dr. Consulta · subscription platform
Eight years in production · Challenge B, refactor legacy
InheritedWith the platform
A featureInheritedtwo weeksWith the platformup to three days
A text changeInheriteda developerWith the platforma product manager
MaintenanceInheritedby handWith the platformsupervising the AI
Infrastructure costInheritedthe period before the modernizationWith the platform−75%
Security
Your team sets the limits. Every session is logged.
Coding agents work inside those limits. The platform stops at level 4. It is not a dark factory.
Your supervisionHuman approval wherever your team requires it
Your authorshipEvery session logged, with an author
Your boundaryLevel 4: the team sets the limit; level 5 is a dark factory
Book a demo
Start with what is stuck.
The new project sitting in the backlog, or the old system that has become a burden. The demo shows the agents working from request to deploy, and the deployment starts with the part your team picks.
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