Software engineering with AI teammates
From vibe coding to software engineering.
Kod gives coding agents the context, workflows, quality constraints, and review process needed to produce maintainable software.
Prepare desktop release
- Define release criteria Intent and acceptance checks attached
- Package macOS build Agent run · 18 files changed
- Run release checks Tests and signing verification
- Review generated changes Waiting for release checks
The engineering system around the agents
Faster implementation creates more work to direct and verify.
AI agents can produce code quickly. Building reliable software still requires planning, coordination, testing, review, and an understanding of the system being changed. Kod keeps that process connected.
01 · Agent workflows
Give every agent a defined job.
Problem A prompt can produce code. It does not define how the work should be analyzed, divided, executed, reviewed, or verified.
Kod runs software work through explicit workflows. Planning workflows inspect the request and codebase, resolve implementation decisions, and produce a structured task tree. Implementation workflows assign those tasks to coding agents, collect verification evidence, and send completed work through review.
- Planning, implementation, audit, and review workflows
- Different models assigned to planning, execution, and review
- Explicit handoffs between workflow phases
02 · Intent, epic, and task tracking
Keep the project legible as agents move faster.
Problem Agent work easily becomes a collection of prompts, terminal sessions, and partially completed changes with no reliable view of what is actually done.
Kod connects the original intent to the epics and tasks used to deliver it. Scope, dependencies, acceptance criteria, agent runs, and verification remain attached to the work instead of being scattered across chats.
- Intents record what should change and why
- Epics define outcomes and group implementation work
- Tasks track scope, dependencies, status, and evidence
03 · Quality gates
Define “done” before the work starts.
Problem Coding agents can produce plausible implementations and report completion without checking every architectural constraint, regression risk, or edge case.
Kod makes engineering requirements part of execution. Tasks carry acceptance criteria, scope boundaries, regression checks, and required verification. Completed epics can be reviewed by an independent model and returned for revision when a gate fails.
- Required tests and verification commands
- Scope, architecture, and regression constraints
- Delegated reviews, evidence, and explicit exceptions
04 · Codebase intelligence
Understand the codebase you are directing.
Problem When several agents change unfamiliar parts of a repository at once, it becomes difficult to understand the impact or make informed decisions quickly.
Kod gives you and your agents structured ways to investigate the repository before making changes. Trace execution, inspect boundaries, find implementations, and audit the system using actual codebase evidence.
- Symbols, text, references, callers, and implementations
- Call trees, runtime boundaries, messages, and endpoints
- Architecture, UX, performance, and regression audits
05 · Local agent workbench
Connect the plan to the agents doing the work.
Problem Planning in one tool and running agents in another creates manual handoffs. Context is copied into prompts, progress is hard to follow, and evidence is separated from the task.
Kod connects to coding agents through MCP and provides an integrated terminal for local execution. Assign a bounded task, follow the run, inspect commands and tests, and keep the outputs with the project record.
- Coding-agent integration through MCP
- Task assignment and run tracking
- Terminal, commands, tests, changes, and artifacts together
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