Requirement binding
Capture owner, repo, branch, paths, acceptance criteria, risk class, and approval boundary before execution.
Engineering agent control plane
Run AI coding agents from one governed workspace.
AgentFoundry gives software teams one workspace for requirements, repo context, execution authority, tool permissions, checks, review evidence, approval, safe-stop, and PR handoff.
Execution and source systems remain hidden machinery. Teams see one engineering workflow, governance controls, review evidence, and clear owner decisions.
Coding Agent Executionscoped tools · evidence onsafe-stop ready
01Planner laneTurns a human requirement into tasks, dependencies, constraints, and safe-stop points.02Code laneProduces bounded diffs under explicit branch/path authority.03Terminal laneRuns commands under policy and records input, output, duration, exit code, and record IDs.04Browser laneCaptures UI state, screenshots, console logs, network evidence, and egress notes.review.reportrequirement.bound · lease.active · command.captured · check.passed · approval.waiting
Command modules
Inspect Evidence ReportEvery module answers: what can the agent do, what evidence exists, and who decides next?
AgentFoundry keeps the visible product simple: plan the run, execute it safely, govern repo/tools/actions, and manage outcomes with evidence always visible.
Authority leases
Grant scoped branch, path, tool, and side-effect permissions to each coding-agent lane.
Evidence stream
Attach commands, diffs, traces, checks, policy denials, screenshots, and reviewer notes.
Decision dock
Approve, request changes, narrow scope, retry, hold, or stop safely with evidence beside the controls.
Control-plane stack
Reliable AI coding work needs governed execution, not another chat box.
A trusted engineering workflow needs a requirement, execution boundary, tool policy, verification, approval, safe-stop, and an Evidence Report before sensitive action.
01Plan
Describe the issue, pick an engineering agent or blank agent, and define the needed outcome in plain language.
02Configure
Set repo, tools, data, memory, escalation rules, allowed actions, checks, and review requirements.
03Run
Launch the agent in a managed, private, or customer-controlled engineering environment.
04Verify
Track tasks, commands, diffs, checks, approvals, failures, and escalations.
05Approve
Approve sensitive steps, adjust permissions, stop, retry, route, or revert.
06Improve
Improve templates, promote safe behaviors, keep ownership clear, and reuse proven engineering agents.
01Planner lane
Turns a human requirement into tasks, dependencies, constraints, and safe-stop points.
evidence required02Code lane
Produces bounded diffs under explicit branch/path authority.
evidence required03Terminal lane
Runs commands under policy and records input, output, duration, exit code, and record IDs.
evidence required04Browser lane
Captures UI state, screenshots, console logs, network evidence, and egress notes.
evidence required05Quality lane
Runs checks, classifies failures, stores logs, and blocks handoff when evidence is incomplete.
evidence required06Review lane
Packages residual risk, approval request, safe-stop route, and next allowed human decision.
evidence requiredSDLC WorkGraph
The system of record for engineering-agent work: requirements, tasks, dependencies, tool calls, commands, changed files, risks, approvals, and review state.
Governance Core
The authority layer for agent identity, repo access, execution locks, policy gates, escalation, spend limits, and human approval.
Evidence Report
An Evidence Report shows what an AI engineering run was asked to do, what it changed, which checks ran, what failed, what risks remain, what it cost, and what needs approval.
Execution layer
AgentFoundry lets teams use different coding tools while keeping the same review, approval, and evidence process.
Tool permissions
Permissioned access to repos, issue trackers, CI, SAST/SCA, docs, browsers, APIs, cloud, governed tools, and approved internal systems.
Engineering Memory
Persistent repo context, policies, examples, report templates, owner decisions, and reusable engineering-agent lessons.
Pick the narrow engineering workflow
Capture the recurring issue type, owner, repo, systems, current cycle time, quality gap, and what must stay human-approved.
Write the coding-agent blueprint
Specify role, repo, inputs, allowed tools, memory, outputs, escalation rules, success metric, refusal boundary, and execution target.
Assemble the engineering run line
Select only what the job needs: tools, UI, sandbox, workflow graph, approvals, and evidence capture.
Run pilot cases with review evidence
Build the working agent, replay real issues, collect traces, changed files, checks, failures, cost notes, and safe-stop instructions.
Manage the release decision
Ship the clear report, owner checklist, source-controlled changes, merge/release risks, go/no-go recommendation, and the next iteration backlog.
Trust boundaries
AgentFoundry ships useful AI coding work with control, not blind automation.
The process makes approvals, evidence, and deployment boundaries first-class before a team scales an agent.
Human-governed by defaultRepo changes, branch pushes, PRs, merges, production changes, spend, external sends, and policy-sensitive steps require an approval path.
Evidence before scaleAn agent earns more autonomy only after traces, checks, risk notes, and owner review prove it can handle the engineering workflow.
Sovereign deployment pathThe pilot can land as a repo workflow, API, internal dashboard, private runner, or PR process depending on the customer boundary.
Pilot checks
Keep the first pilot narrow enough to prove.
A useful pilot has one engineering workflow, one owner, explicit repo/tool boundaries, real run evidence, and a stop/merge/retry decision.
One engineering workflow, one owner, one baseline, one measurable handoff
Repos touched, tool permissions, and human approval gates are explicit
Representative issues have traces and changed files
Quality checks include failures, not only wins
Safe-stop, monitoring, and improvement loop are documented
Engineering pilot intake
Bring one Issue-to-PR or code-review workflow.
Share the repo, issue pattern, baseline, approval rules, and success metric. AgentFoundry converts it into a governed coding-agent pilot.