
Intake
Catch work from where it already appears
Slack threads, meetings, docs, repos, errors, CRM notes, and user feedback already contain engineering work. Kodan turns the useful signals into inspectable tasks.
See this workflowFor engineering teams using AI coding agents
Kodan catches work, scopes ambiguity, prepares early solutions, and keeps review evidence visible as your process becomes a software factory.

Observable pipeline

Intake
Slack threads, meetings, docs, repos, errors, CRM notes, and user feedback already contain engineering work. Kodan turns the useful signals into inspectable tasks.
See this workflow
Routine
Small copy fixes, simple bugs, missing checks, cleanup, and dependency chores should arrive with a proposed solution before they steal engineering focus.
See this workflow
Scoping
When work is ambiguous, Kodan gathers context, names unknowns, asks questions, creates acceptance criteria, and makes the decision legible.
See this workflow
Review
Implementation should not be a blind diff. Kodan keeps changed files, commands, checks, costs, risks, and before/after behavior close to the task.
See this workflowControl surface
Capture
Connected SourcesCapture mode
Kodan watches approved inputs and turns useful signals into candidate tasks.
Product proof

Risks, assumptions, and feature-bloat notes make vague work decidable before anyone starts coding.

Kodan plugs into the places work already appears: repos, docs, meetings, chat, tickets, and browser notes.

Small bugs and straightforward changes can arrive with a proposed fix before they become an engineer interruption.

Agent output should carry changed files, commands, passing checks, and enough context to review it.

Kodan can update a task, preserve the user's intent, and route the work through the connected systems already in the project.
Transparent usage
Each task exposes what ran, what it cost, and why. Observability should include pipeline state and cost state.
Pay the model and runtime cost Kodan spends, plus a clear platform margin.
Local or private deployments, internal models, custom data controls, and security review.
See your pipeline
Experimental workflows