System capabilitiesAgent workflowsEvidence ReportsPilot planGet run notesDescribe pilot workflow
AgentFoundry

Govern engineering agent systems from objective to handoff.

Plan. Execute. Verify. Govern. Handoff. Agents move scoped engineering work; humans set objectives, constraints, risk, and approval.

Run governed AI engineering agents and specialized agents inside visible, bounded, evidence-backed engineering workflows.

Describe the pilot workflowDiscuss an engineering constraintDescribe integration needReview run loopInspect Evidence Reports
AFENGINEERING_AGENTS · governed runawaiting approval
AgentFoundry work loopgoverned autonomous engineering workrunning
03Plantracked
04Executetracked
05Validatetracked
06Evidencetracked
07Approvetracked
run logobjective set · context loaded · agents coordinating · checks running · report compiling · approval waiting
coordinate agentsfrom scoped objective to review-ready work
reduce coordination loadrepeatable CI, review, docs, and triage agent runs
see proofEvidence Reports for governed runs your team can review
test one use casestart with one real workflow, urgency, and outcome
keep controlhuman approval at risk boundaries
Governed autonomous engineering

Let engineering agents move more tasks without making the work harder to govern.

Teams get faster issue-to-review movement, less repetitive coordination, clearer review notes, and human control over risky decisions.

Coordinate engineering agents without losing control

Move scoped implementation, triage, fixes, docs, and migration tasks into visible agent runs while humans keep approval authority.

Reduce human coordination load

Move recurring CI, review, documentation, and issue-prep work into repeatable agent operations that stay visible.

Increase review visibility

Give reviewers agent plans, changed artifacts, commands, checks, failures, and risk notes instead of a vague AI transcript.

Scale engineering work before scaling headcount

Use bounded engineering-agent runs while keeping repository limits, review rules, and human decisions explicit.

Agent work loop

From objective to governed handoff.

Every run shows the objective, agent plan, what changed, which checks ran, what failed, and where a human must approve.

01

Intent

Define the engineering objective, repository boundary, risk level, and accountable human owner.

02

Context

Attach codebase, docs, CI signals, architectural constraints, and relevant workflow history for the agents.

03

Plan

Specialized agents break work into reviewable steps with dependencies, check commands, and human approval points.

04

Execute

Engineering agents make bounded changes while files, actions, and progress stay visible.

05

Validate

Builds, tests, review checks, and policy scans verify the work until it is ready to review.

06

Evidence

Compile an Evidence Report showing what agents changed, what passed, what failed, and what still needs review.

07

Approve

Humans retain decision rights for risky changes, releases, credentials, and production boundaries.

08

Handoff

Deliver source-controlled engineering work with test results, review notes, and a clear approval state.

Use cases

Start where human coordination already limits engineering work.

Start with one repo, real checks, clear owners, acceptance criteria, and a task ready for governed agent work.

Issue to review

Move a scoped issue from request to source-controlled change with tests and review notes.

Code review

Check implementation quality, security posture, regressions, and policy fit before approval.

CI failure repair

Reproduce a failing pipeline, isolate the cause, apply a focused fix, and return evidence.

Migration projects

Modernize legacy code in bounded slices without losing context, compatibility, or rollback discipline.

Documentation

Update engineering docs, release notes, and review notes from the actual run context.

Bug triage

Classify incoming defects, reproduce failures, identify owners, and prepare repair plans.

Platform engineering

Move internal engineering workflows forward while keeping policy, approvals, and visibility intact.

Release handoff

Prepare check summaries, risk notes, and approval checkpoints for controlled delivery.

Engineering workflows

Most Common Engineering Workflows

Start with recognizable engineering work before inspecting the run loop and evidence model.

Issue to review

CI failure repair

Code review preparation

Dependency upgrades

Migration projects

Bug triage

Release handoff

Documentation generation

Engineering outcomes

Engineering Outcomes

The outcome is governed engineering movement with visible evidence and human approval boundaries.

Faster issue-to-review movement

Better review visibility

Reduced engineering coordination

Repeatable engineering workflows

Clear approval boundaries

Traceable engineering execution

Illustrative example

Illustrative Evidence Report

Clearly labeled illustrative example. This is not presented as an actual customer result.

Illustrative report summary

  • Issue: CI failure after dependency update.
  • Plan: Reproduce and isolate root cause.
  • Execution: 3 files modified.
  • Validation: 27 tests passed.
  • Risk: Low.
  • Status: Ready for review.
Evidence Reports

Make agent work visible before approval.

The Evidence Report gives reviewers one place to see the objective, agent plan, changed files, test results, failures, risks, and next approval step.

Evidence Report includes

  • objective and accountable owner
  • changed files, configs, docs, and scripts
  • commands, tests, checks, and review rules run
  • failures, fixes, unresolved blockers, and risk notes
  • human approval decision and next step

Sample run record

  • Issue: CI failure on a real repository workflow
  • Plan: reproduce, isolate cause, patch the smallest safe change
  • Execution: changed files and commands stay attached to the run
  • Validation: tests, failures, retries, and review checks are recorded
  • Approval: human reviewer decides whether to PR, narrow, retry, or stop
Editions

Start small. Expand only after the evidence is real.

Start with one team and one workflow. Expand only when the first run proves it saves time without adding risk.

AgentFoundry

AgentFoundry Pro

For builders and small teams proving one governed engineering-agent workflow.

Start with real repositories, repeatable agent work, visible runs, and review-ready reports.Describe pilot workflow
AgentFoundry

AgentFoundry Enterprise

For organizations rolling out governed autonomous engineering work across teams and production boundaries.

Add enterprise controls, approval policy, deployment flexibility, audit needs, and guided pilot design.Describe pilot workflow
FAQ

Trust, scope, governance, and adoption questions.

Answers for buyers checking scope, control, proof, and rollout risk.

Is AgentFoundry an editor helper?

No. Editor helpers work while a developer types. AgentFoundry coordinates engineering-agent runs that can plan work, make changes, run checks, debug failures, and prepare review notes for a human decision.

Who should evaluate it first?

CTOs, VP Engineering, platform leaders, AI engineering leads, and founders who already see AI coding tools helping but need more reliability and control.

What should we bring to a pilot?

Bring one real repo, one painful engineering task, current CI checks, access limits, and the approval rules that matter before work is accepted.

Can humans stay in control?

Yes. The product model keeps humans accountable for risk boundaries, approvals, credentials, releases, and production decisions.

What is the main trust record?

An Evidence Report that shows the objective, agent actions, changed files, commands run, checks passed or failed, blockers, risks, and what needs human approval.

Pilot plan

Four weeks to prove whether one engineering-agent workflow becomes easier to govern.

A pilot should show whether AgentFoundry can reduce coordination load, improve review visibility, and keep human control on one real workflow.

Week 1

Select one painful workflow

Choose a real repo, current CI checks, owners, access boundaries, and the acceptance criteria for work.

Week 2

Configure agent and review boundaries

Map planning, coding, debugging, testing, review, and delivery responsibilities with repository, sandbox, and tool limits.

Week 3

Run the agents

Move bounded engineering tasks with visible agent progress, checks, blockers, and human approvals.

Week 4

Review evidence

Compare results against buyer outcomes: toil reduced, review effort lowered, issues moved faster, and risk stayed controlled.

Issue-to-PR cycleMeasure whether scoped tasks move faster from accepted issue to review-ready change.
Review burdenMeasure whether reviewers get clearer evidence and spend less time reconstructing what happened.
Engineering toilMeasure recurring work shifted from manual coordination to controlled reviewable runs.
Would AgentFoundry help with this use case?Capture the painful task, desired outcome, urgency, decision owner, and API/integration need before proposing scope.
Control retainedVerify repository boundaries, credentials, approvals, and release decisions stayed with accountable humans.
Evaluation paths

Choose the smallest path that creates a practical next step.

Bring one real engineering task, what is blocked today, and what result would make the next step worth it.

Pilot

Bring one real engineering task, current blocker, acceptance criteria, urgency, and approval rules.

Workflow conversation

Use contact when the right first workflow is unclear but the pain is real.

API access

Request API access when integrations, SDKs, external systems, or automation boundaries matter first.

Subscribe

Send a lighter workflow note when the team is still evaluating but wants practical updates.

Enterprise pilot

Prove one governed engineering-agent workflow.

Bring one repo, CI/CD workflow, issue stream, or release process to map a safe pilot.

Send one engineering job privately. We will review fit before proposing a narrow next step.