Put Descant to work on your codebase
Give your team a shared, fully autonomous software engineering system that produces better code than frontier models alone. Connect your GitHub repository, assign the work and follow the progress, resulting changes and cost in one place.
You choose what Descant takes on
Start with a specific issue, or make work available for continuous development. Set the priorities and give Descant the refactor, migration, feature or larger initiative you want carried through.
For larger assignments, Descant breaks the work into manageable issues and coordinates their dependencies. Independent work can move forward in parallel, while dependent changes follow in order.
Increase capacity as the work demands it, with the same engineering discipline applied across the system.
Keep the whole initiative moving
Large engineering initiatives depend on getting related changes to work together. Descant coordinates that work through Issuegraph, making dependencies, priorities and opportunities for parallel execution explicit.
Explore the relationships behind the work. See what can move forward, what needs to happen first and how changing a dependency changes the order.
Increase your limit in the dashboard.
Descant holds the work to a higher engineering standard.
It examines the system around each assignment, challenges the first plausible solution, and pursues the simplest approach that solves the whole problem. Architecture, failure cases, maintainability and the consequences for surrounding code are part of the work. Structured evidence and software-enforced checks carry that standard through implementation, verification and independent review.
A requirement needs a corresponding implementation and verification. A review finding needs a recorded resolution. A claim of simplification needs evidence of what became simpler. Descant builds those obligations into the system, so the engineering process has substance beyond instructions in a prompt.
Those obligations have consequences in the software. Claimed fixes are checked against the resulting changes, and unverified claims remain visible to review. Findings carry recorded responses or remain identified as unresolved. Independent approval is tied to the work being merged.
Your team can inspect the changes, feedback and responses through the familiar GitHub workflow.
Built from engineering experience and measured results
Descant grew out of years of building software alongside exceptional engineers, challenging each other’s approaches, and learning what survives real complexity. We’ve combined that experience with extensive testing of how models succeed and fail, then built the resulting engineering practices into an autonomous system.
Built to advance with the models
Greater intelligence does not automatically produce comprehensive, dependable engineering.
Intelligence cannot reliably recover information that is genuinely absent from everything it can observe or already knows. It can infer, estimate, or recognize uncertainty. It cannot make an unknown fact certain through better reasoning alone.
If two systems look identical from the available information, but an undiscovered dependency makes the correct change different in each, even perfect reasoning cannot distinguish them. Something has to uncover that dependency.
That gives us a concrete distinction in engineering:
- Reasoning: drawing better conclusions from available evidence.
- Investigation: acquiring evidence that wasn’t available.
- Verification: producing evidence by exercising the actual system.
- Memory: preserving discoveries so later work can use them.
A process that systematically discovers, produces, and preserves relevant information supplies something intelligence alone cannot substitute for.
A stronger model could take responsibility for that process itself. But it would still have to perform the investigation, run the checks, and retain the findings. Those activities cannot disappear simply because it became smarter—and it cannot be assumed those activities will be systematically performed by prompting alone.
Descant makes those responsibilities explicit and runs the process in software, with the depth adapted to the assignment. Structured responses are validated, findings stay connected to recorded outcomes, and claimed fixes are checked against the resulting changes. The engineering standard has machinery behind it.
What changes as models improve?
Some parts of today’s system will become unnecessary as models improve. We expect that, and we want it. We can remove machinery that no longer improves the result and put more effort into helping stronger models accomplish work that was previously out of reach.
Descant is built to change with that boundary. Models can be replaced across the system, and we evaluate the combinations and engineering practices around them against the quality and cost of the work. The aim is to spend progressively less effort compensating for limitations and more effort multiplying capability.
Anthropic’s research describes this shift in practice: stronger models made some scaffolding unnecessary, while planning and evaluation continued to help on work beyond the model’s reliable baseline. The authors recommend removing components that no longer improve performance and developing new ones as capabilities advance.
What stays accountable?
Production software still has to meet the requirements, preserve the behavior people depend on, and come with evidence appropriate to the change. Work running autonomously at scale also needs its dependencies, findings, approvals, retained knowledge and costs kept coherent.
The way those responsibilities are fulfilled will evolve. Their fulfillment must still be established through the work. Greater model capability alone does not establish that a particular assignment was investigated, verified or completed.
With Descant, your team gets both the advances in model intelligence and a system responsible for carrying the engineering through. We welcome the improvements that let us simplify that system and deliver more value from every unit of work.
The next change starts with better context
Descant records useful project knowledge and the reasoning behind important decisions so subsequent work can build on what has already been learned.
When it discovers valuable work outside the current assignment, it creates a separate issue. The current change stays focused, and the discovery becomes visible work the team can prioritize.
As the codebase evolves, Descant keeps the follow-up work it creates organized, identifying duplicates and reconciling work that has become stale or has already been addressed.
Your checks and merge rules stay in force
Issues, branches, pull requests, reviews and required checks remain in GitHub. Your team chooses which work Descant can take on and how approved changes reach the codebase.
Enable automatic merging for approved pull requests, or keep the final merge with a person. Your existing branch protections and repository rules continue to apply.
The dashboard gives your team a shared view of active work, completed changes and spending. Follow the work from your desk or your phone, with costs tracked down to the cent.
Keep the agents your team already uses
Research, ideate and work through system architecture, technical designs, specifications and protocols with your favorite agents. We encourage it, and it's how we use Descant ourselves. Turn that work into issues with requirements and acceptance criteria, then hand implementation to Descant.
Through the API, CLI and MCP, those agents can file issues, follow progress and report back while Descant executes the work. Descant turns requirements and acceptance criteria into structured data that software quantifies and verifies, not a model's assertion. Agents can synthesize research and evidence, but model intelligence can't replace information that is truly missing.
Clear boundaries around autonomous work
Descant separates customer-code execution from model and repository access, restricts execution-network traffic, and delivers changes through your repository’s pull-request workflow. Your team controls access and automatic merging.
Is our code used to train Descant?
Your repository is used to perform the work you request. Customer code is not used as training data for the product.
Start with an issue worth solving
Give Descant a real piece of work. Inspect the code, review the result and see what delivering it cost.