AI Coding Governance
Govern, gate, and learn from every AI-written change
Your AI writes the code. DecisionLedger governs the decisions, gates the risk, meters the cost, and remembers the outcomes. Every pull request is risk-classified from its diff, and the policies you author decide what happens next: hold high-risk changes for human review, require a linked decision or project, route to a committee, trigger an impact assessment, or attribute the spend to a cost center. Every revert or failed build becomes a lesson your agents read before they touch that code again.
The risk gate, live
Every pull request, classified and gated
Watch a batch of AI-authored pull requests get scored from their diffs. The risky ones are held for a human, the safe ones merge, and past lessons are recalled before the next change is written.
Challenges We Solve
Common pain points that structured decision models eliminate.
AI Ships Code Faster Than You Can Review It
Agents open pull requests around the clock, and every one lands on a human who cannot read them all. Classify each change by risk from its diff so reviewers spend their attention only where it matters.
The Risky Changes Look Like All the Others
A one-line auth change and a typo fix arrive as identical pull requests. Auth, migrations, infrastructure, and secrets are flagged high or critical automatically, and held for human sign-off before they can merge.
Your Agents Keep Repeating the Same Mistakes
The change that was reverted last month gets written the same way again, because nothing remembers it. Reverts and failed builds are captured as lessons and surfaced to the next agent before it touches that code.
Policy Lives in a Wiki No Agent Reads
Your coding standards sit in a doc the AI never opens. Publish them as rules your agents read at the start of every task, and as a governance gate that enforces them at merge.
One Gate for Every Change Is the Wrong Gate
A single hard block treats a docs typo like a schema migration and slows everyone down. Author policies from triggers, actions, and an advisory or required posture, so each kind of change gets exactly the oversight it warrants.
AI Coding Spend Nobody Can Attribute
Agents burn tokens across dozens of projects and the bill arrives as one undifferentiated number. Attribute each change's AI spend to its project and cost center, and gate work that would blow a budget before it runs.
Use Cases
How teams use DecisionLedger to make better decisions.
Lets agents open pull requests at scale while every high-risk change is held for human review and every decision is audited.
Velocity without ungoverned risk
Sees each pull request's risk tier, the reasons it fired, the changed files, and the lessons from prior changes to the same code, right where they review.
Context to approve or block in seconds
Connects any coding agent through MCP and a pre-commit hook, so risk classification and org rules travel with the agent into the editor and the pipeline.
One governance layer across every agent
Attributes each AI-written change to its project and cost center and sets budget gates, so AI-assisted engineering spend is allocated, forecastable, and held inside its envelope.
AI coding spend attributed and capped
Governing an AI pull request, before and after DecisionLedger
See how agent orchestration compresses an 8-week manual process into same-day resolution.
Without a governance layer
Every PR triaged by hand
One reviewer, no memory, no cost view
With DecisionLedger
Risk, cost, and policy surfaced the moment the PR opens
Attention only where it matters, with memory
Measurable Impact
Based on platform benchmarks across early adopters.
Change Risk
Every PR looks the same
Classified from the diff
High-Risk Merges
Merged and hoped
Held for human sign-off
Repeat Mistakes
Rewritten from scratch
Recalled as cautions
Coding Policy
A wiki no agent reads
Rules agents follow
Governance Depth
One gate for every change
Policy per change type
AI Coding Spend
One unattributed bill
Attributed to cost centers
A Governance, Cost, and Memory Layer for AI-Assisted Engineering
DecisionLedger does not write your code. It governs the decisions behind it, gates the risk, meters the cost, and remembers what worked, around whatever coding agent your team already uses.
Configurable Policy Engine
Compose policies from a trigger, one or more actions, and an advisory or required posture. Every ingested pull request is evaluated, so oversight scales from a docs typo to a schema migration without a one-size gate.
Risk-Gated Pull Requests
Every change is classified from its diff and high-risk work is held for human review, with a required check that gives the gate real teeth.
Decision and Project Governance
Require that a significant change link to a tracked decision or project before it merges, or have the policy open the decision record automatically so nothing ships unattributed.
Committee and Impact Review
Route qualifying changes into a governance committee for a vote, or trigger an AI-impact assessment, so the highest-stakes AI work carries the review a board expects.
Cost Attribution and Budget Gates
Attribute each change's AI spend to its project and cost center, and gate work that would exceed a budget before it runs, so AI-assisted engineering stays inside its envelope.
Outcome Memory and Org Rules
Reverts, failed builds, and recorded decisions become lessons your agents recall, and your coding rules ship as an AGENTS.md every agent reads before its first edit.
Built for every engineering role
Whether you ship the code or govern it, DecisionLedger gives your role the controls and the context it needs.
VP Engineering / Head of Platform
- Let agents open pull requests at scale without merging ungoverned risk.
- Author policies from triggers, actions, and an advisory or required posture, and tighten them as trust grows.
- A required status check gives the gate real teeth on your protected branches.
- Every gated decision is sealed with a tamper-evident attestation you can audit later.
Staff Engineer / Reviewer
- See each pull request's risk tier and the exact reasons it fired, not just a red or green check.
- Read the reverts and CI failures tied to the same files, right where you review.
- Approve or block from the review queue or a single comment on the pull request.
Head of AI / Developer Experience
- Bring any coding agent through MCP and a pre-commit hook. No rip and replace.
- Risk classification and your org rules travel with the agent into the editor and the pipeline.
- Publish coding standards as an AGENTS.md every agent reads before its first edit.
Security & Compliance
- Auth, secrets, and infrastructure changes are flagged and held for sign-off before they merge.
- Keep a complete, tamper-evident trail of who approved which AI-written change and why.
- Show evidence that AI-generated code passed human oversight, ready to map to your controls.
- Route the highest-stakes changes to a committee vote or an impact assessment, on policy, not by memory.
Engineering Finance / FinOps
- See every AI-written change attributed to its project and cost center, not a single unallocated bill.
- Set budget gates that hold work which would exceed a cost envelope before it runs.
- Forecast AI-assisted engineering spend from real per-change attribution.
Bring your own agent
Works with the stack you already have
No rip and replace. DecisionLedger sits between your coding agents and your Git host, CI, and editors, so governance travels everywhere your agents write.
Coding agents
Source & CI
Where your team works
How It Works
Three steps to structured, auditable decisions.
Classify
Every pull request is scored from the files it touches. Auth, migrations, infrastructure, and secrets rank high or critical; tests and docs stay low. Files that were reverted or broke CI before rank higher than their path suggests.
Gate
The policies you author evaluate every ingested pull request. Depending on what the change touches, a policy can hold it for human review behind a required status check, require a linked decision or project, open one automatically, route it to a committee, trigger an impact assessment, or attribute its cost. Each action runs advisory-first or required, and every gated decision is sealed with a tamper-evident attestation.
Learn
Reverts and CI failures are captured automatically as outcomes and turned into lessons. Your agents recall them, and read your organization's rules, before they write the next change.
When an AI agent opens fifty pull requests this week, can you say which ones are risky, which changed code that has broken before, which need a linked decision or a committee vote, and what each one cost, or is it all landing on one reviewer to sort out?
Manual PR triage
Every AI pull request lands on a human with no signal of which ones are risky
Code review bots
Comment on style and bugs, but do not gate risk, route to a committee, or remember outcomes across changes
Branch protection alone
Blocks on generic checks, blind to what the change touches, its history, or its cost
Standards in a wiki
Coding policy the AI never reads and nothing enforces at merge
Spreadsheet cost tracking
AI coding spend guessed after the fact, never attributed to a project or gated before it runs
Ad-hoc decision logs
No link between a risky AI change and a tracked decision, committee vote, or impact assessment