AI Coding Governance
Govern, gate, and learn from every AI-written change
Your AI writes the code. DecisionLedger governs the decisions, gates the risk, and remembers the outcomes. Every pull request is risk-classified from its diff, high-risk changes are held for human review, and every revert or failed build becomes a lesson your agents read before they touch that code again.
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.
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
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
A Governance and Memory Layer for AI-Assisted Engineering
DecisionLedger does not write your code. It governs the decisions behind it, gates the risk, and remembers what worked, around whatever coding agent your team already uses.
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.
Outcome Memory
Reverts, failed builds, and recorded decisions become lessons your agents recall, so the codebase gets safer the longer you use it.
Policy Your Agents Follow
Publish your coding rules and governance gates as an AGENTS.md and a live tool, so every agent starts each task knowing them.
Connects With
Part of 150+ native integrations across CRM, marketing, finance, HR, ecommerce, and analytics
Salesforce
HubSpot
Stripe
Shopify
Google Analytics 4
Workday
QuickBooks
Snowflake
Slack
Zendesk
GitHub
Meta Ads
Mailchimp
NetSuite
Jira
Power BISalesforceHubSpotStripeShopifyGoogle Analytics 4WorkdayQuickBooksSnowflakeSlackZendeskGitHubMeta AdsMailchimpNetSuiteJiraPower BIHow 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
High and critical changes are held for human review before they can merge, with a required status check on your branch. Reviewers approve in the queue or with a comment on the pull request, and every 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, and what your agents learned from the last failure, 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 or remember outcomes across changes
Branch protection alone
Blocks on generic checks, blind to what the change touches or its history
Standards in a wiki
Coding policy the AI never reads and nothing enforces at merge