AI-assisted software delivery governance
AI coding tools help engineering teams move faster. Hakama is a governance layer that turns AI-assisted software delivery into a policy-and-evidence runtime.
The value is simple: every AI run prompt is managed by an operating boundary. Teams keep their preferred tools, developers keep momentum, and reviewers get the proof they need to accept work with confidence.
The problem
With the volume of commits AI agents can create, how do you know an agent did not introduce future technical debt?
Code reviewers have to reconstruct scope, intent, constraints, risk, evidence, and test coverage after the agent has already acted. For small changes, that may be manageable. For production systems, regulated environments, or large teams, it becomes a control problem.
Hakama gives solo users and teams a more disciplined path. It captures telemetry on every governed AI agent run.
How Hakama works
01
Hakama can run an intent intake to convert the developer's prompt into a scoped work plan.
That scope becomes the operating frame for the AI agent to work within. Hakama also creates telemetry that the team can review.
02
Hakama watches the AI agent session and pushes agent responses through read and write governance checks.
Hakama works around AI agents so local agent workflows stay familiar while still being governed.
03
Hakama has an agent-response evaluator that evaluates the telemetry against the developer's change intent and task focus.
This creates a practical enforcement point before drift becomes a problematic mutation and reduces the need for reviewers to reverse-engineer what the AI agent produced.
04
Before a change moves forward, Hakama runs delivery gates: scope, assumptions, QA expectations, policy authority, provenance, receipts, and release readiness.
The result is repeatable governance that provides telemetry for: what was requested, what was allowed, what evidence existed, what passed, what failed, and who approved the next step.
Evidence and receipts
Hakama produces durable evidence around the work. It can record feature scope, execution scope, gate results, QA expectations, plugin evidence, delivery status, and receipt verification.
Telemetry receipts give code reviewers a way to inspect the path from code change to result. Developers and teams get a structured trail tied to the change instead of relying on developer memory or scattered chat context.
This telemetry makes governance repeatable. Every accepted change can carry a record of what happened, what was checked, and why it moved forward.
Extensible evidence
Hakama does the governance work, and the plugins give it more ways to gather proof. Plugins help Hakama understand architecture, document APIs, do intake for greenfield starts, assess brownfield risk, check security signals, detect drift, size regression risk, or prepare QA context.
Hakama keeps the governance decision path consistent while plugins add the telemetry receipt evidence that developers and teams need for the review process.
What teams get
Where Hakama fits
Hakama fits around existing developer tools and delivery workflows. Developers can keep using their coding agents, editors, terminals, pull requests, and CI systems.
The change is the control layer around the work. Hakama gives teams a way to define scope, enforce boundaries, collect evidence, and prove readiness before the change moves forward.
For solo developers, Hakama can provide local discipline and structured artifacts. For teams, the same model extends into policy, approvals, shared evidence, and operational review.
For the whole team
Hakama supports the full software delivery team around an AI-assisted change: developers, code reviewers, QA, product, engineering leaders, platform owners, and security/compliance.
Checks the AI agent: files, dependencies, routes, schemas, approvals, policy state, and supporting evidence.
Gives QA, Product, and Engineering Managers a place to see what happened, understand the risk, add notes, acknowledge results, and make acceptance decisions.
Policy Admins can manage policy packs, approval queues, governed repos, audit views, incidents, entitlements, and seat settings.
Easy access to provisioning and licenses for providing access to Hakama for your team.
Built for serious engineering environments
Hakama is useful anywhere AI-assisted work needs a controlled path into the codebase. It is especially valuable for teams handling production systems, regulated software, sensitive infrastructure, platform governance, or high review volume.
Visibility into how AI-assisted changes are scoped, checked, approved, and delivered.
A consistent policy layer around local agents, repo changes, evidence, and delivery readiness.
Structured context: what the request was, what the agent did, what evidence exists, and where the risk sits.
Durable artifacts that support review, investigation, incident response, and audit.
Why it matters
AI agents can move work forward quickly. Hakama adds trust by tying each run to the request, the policy, the evidence, and the approval path.
The result is a controlled workflow where engineers keep momentum, reviewers get better context, and organizations gain proof around the work entering the codebase.
Frequently asked questions
Hakama is for engineering teams using AI agents that want control and want to preserve the long-term gains they get from AI-assisted delivery.
Hakama is built for environments where scope, evidence, review, and delivery control matter.
Hakama governs the path from request to accepted change: intake, scope, agent session behavior, write readiness, gate evidence, receipts, and commit readiness.
Hakama gives code reviewers a structured trail of intent, constraints, evidence, gate results, and delivery status, so review starts with context instead of reconstruction.
Teams should start with a pilot. Pick one project that could benefit from an AI-assisted workflow, then run a set of changes through Hakama and review the evidence.