The engineering system for AI agent teams
AI agents write code. Kod helps them build software.
Kod gives teams of AI agents the structure, processes, and tools to plan work, understand the codebase, implement safely, and prove the job is done.
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
Fast generation is not finished software
Agents need more than a prompt.
Left on their own, agents produce plausible code but often miss the difficult last mile: architectural fit, edge cases, tests, and review. Like junior engineers, they do their best work with clear scope, useful tools, and experienced oversight. Kod brings those engineering disciplines to every stage of the work.
01 · Planning sessions
Turn intent into an executable plan.
Problem A prompt is not a plan. Agents need to understand the system, resolve decisions, define scope, and identify what could break.
Kod runs structured planning sessions that explore the codebase and turn an intent into ordered tasks with dependencies, acceptance criteria, and verification requirements.
- Explore before changing
- Resolve important decisions early
- Define what “done” means
- Produce work agents can execute safely
02 · Structured implementation
Keep agents focused on finished outcomes.
Problem Loose agents optimize for producing code. They can stop before tests, edge cases, documentation, and integration work are complete.
Kod runs implementation sessions against clearly defined tasks. Every agent receives the relevant context, constraints, expected outcome, and required checks.
- Bounded tasks with explicit scope
- Context carried from planning into implementation
- Progress and evidence attached to the work
- Clear handoffs between agents and phases
03 · Review and quality
Generated is not done.
Problem An agent reporting completion is not evidence that a change is correct, maintainable, or safe to ship.
Kod applies software engineering quality gates throughout implementation. Tests and verification are required, completed work is independently reviewed, and failed reviews return to implementation.
- Acceptance criteria checked before completion
- Tests and regression checks recorded
- Independent review of completed work
- Revision loops when quality gates fail
04 · Code intelligence for agents
Give agents better ways to understand code.
Problem Text search alone makes agents waste time and context. It also makes it easy to miss callers, boundaries, dependencies, and runtime behavior.
Kod gives agents static analysis tools for exploring symbols, references, call trees, endpoints, messages, and runtime boundaries—so they can understand impact before editing.
- Find definitions and references precisely
- Trace execution through the system
- Discover APIs and runtime boundaries
- Make decisions from codebase evidence
05 · Project intelligence for engineers
Keep humans oriented as agents move faster.
Problem When several agents plan and change code at once, understanding the system and reviewing their work becomes harder.
Kod builds an LLM-powered project wiki and codebase analyses that help engineers explore architecture, understand unfamiliar areas, and evaluate changes with better context.
- Living project and architecture knowledge
- Guided exploration of unfamiliar code
- Codebase health and quality analyses
- A shared view for humans and agents
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AI agents need an engineering system.
Kod brings structure to planning, discipline to implementation, and review to every change.
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