AgentFoundryGoverned AI engineering agents

Put AI agents to work in engineering without losing control.

AgentFoundry helps engineering teams move from issue to review-ready work with planned execution, validation, Evidence Reports, human approval, and governed handoff.

Start enterprise pilot See operating loop View proof

UseStart enterprise pilotStart with the product or page that helps you act.InspectSee operating loopInspect the workflow, model detail, or evidence when trust depends on it.DecideStart enterprise pilotLeave with a concrete next step, not a vague brand impression.

Faster

Move issues

Evidence

Review

Approval

Control

Evidence Reports

Make agent work reviewable before teams approve it.

The trust object is the Evidence Report: intent, plan, touched files and systems, validation results, failure notes, risks, unresolved questions, and handoff status.

AI engineering agents

Primary category

Plan → Handoff

Operating loop

Evidence Reports

Trust artifact

live proof

01 · plan

Scoped execution plan

The system starts with explicit intent, constraints, repository state, acceptance criteria, and review boundaries.

plan

trace

ship

intent · source state · boundaries

02 · verify

Validation and checks

Tests, static checks, logs, failures, recovery attempts, and changed files stay visible as the run progresses.

tests · logs · diffs

03 · approve

Evidence-backed handoff

Human owners review the Evidence Report before work enters delivery or rollback decisions.

evidence · approval · handoff

Product definition

AgentFoundry is not a coding copilot. It is a governed engineering work system.

AgentFoundry is built for engineering leaders who need AI agents to perform bounded software work while preserving validation, evidence, approval, and operational accountability.

What it does

Turns issues into review-ready work

A scoped task moves through planning, execution, validation, evidence, approval, and handoff.

Why it is different

Evidence before approval

Teams see what changed, what passed, what failed, and what still needs human judgment.

Who it serves

Engineering organizations

Built for teams that need more engineering capacity without hidden automation risk.

Outcome

Review-ready work

AgentFoundry is framed around engineering work completed with enough evidence to review.

Control

Approval gates

Humans retain accountability for what is accepted, revised, rejected, or handed off.

Differentiation

Governed execution

The product is not a generic workspace or chatbot; it is controlled engineering execution.

Operating loop

Plan → Execute → Verify → Govern → Handoff

AI agents can do more engineering work when teams can inspect, verify, approve, and hand off the result.

Plan

Turn intent into a bounded task

A human owner defines the objective, source state, constraints, and acceptance boundary.

Scoped

Execute

Perform bounded engineering work

Agents work against repository state, documentation, dependencies, and delivery requirements.

Active

Verify

Check against expectations

Validation compares outputs against tests, expected behavior, risk, and operational constraints.

Checked

Govern

Keep controls visible

Access, policies, approval gates, logs, and escalation paths stay inspectable.

Controlled

Handoff

Deliver with evidence

Approved work moves forward with an Evidence Report and traceable handoff.

Review-ready

Enterprise control

Governance, evidence, and approval are the product advantage.

AgentFoundry gives high-consequence engineering work a calm operating surface: bounded scope, visible state, evidence, and accountable approval.

Bounded work

Tasks start with explicit scope, constraints, repository state, and success criteria.

Visible state

Plans, diffs, logs, tests, risks, failures, and recovery steps stay visible.

Evidence Reports

The output includes review material, not only changed files.

Human approval

The system supports responsible acceptance decisions without erasing accountability.

Engineering work system

The mechanism matters, but it should sit below the outcome.

AgentFoundry earns its enterprise position by connecting AI execution to validation, governance, evidence, and handoff.

Public surface

AgentFoundry

Product, research, and evidence paths stay easy to choose without turning the page into an architecture map.

Planned execution

The system turns engineering intent into explicit plans, constraints, and acceptance criteria.

Validation

Tests, checks, failures, and recovery attempts become part of the visible work record.

Governance

Policies, permissions, approval gates, and escalation paths keep autonomy controlled.

Evidence-backed handoff

Work moves forward only when the responsible human can inspect the evidence.

Pilot workflows

Start where engineering work already hurts.

The strongest pilots have a real repository, clear checks, responsible owners, and an acceptance boundary.

CI failure repair

Investigate failing checks, propose fixes, run validation, and produce review evidence.

Issues

Issue-to-PR implementation

Move a bounded issue through implementation, tests, and approval-ready handoff.

Security

Security fix preparation

Prepare safe patches, validation records, and risk notes for human approval.

Modernization

Dependency modernization

Upgrade bounded dependencies with checks, rollback notes, and evidence.

Release

Release handoff preparation

Prepare release notes, checks, open risks, and handoff material.

Evidence and approval

Do not trust invisible agent activity. Review the work with evidence.

AgentFoundry turns autonomous engineering work into reviewable artifacts so owners can approve, revise, reject, or roll back with project state.

Scope

Define the bounded workflow.

Choose one engineering workflow with clear inputs, checks, and accountable reviewers.

Run

Execute with visible state.

Keep plans, logs, changed files, validation, and risks inspectable during the run.

Report

Produce Evidence Reports.

Summarize what changed, what passed, what failed, and what remains unresolved.

Approve

Keep humans in control.

Owners decide what proceeds into delivery and what needs revision.

Enterprise value

Ship more engineering work without hidden risk.

Scoped engineering work can move toward review while leaders retain visibility, evidence, and approval. Outcomes come before runtime terminology: faster issue-to-review movement, less toil, and clearer review material.

Faster issue-to-review movement.

Less repetitive engineering toil.

Clearer evidence for review.

Human approval before delivery.

Operational control over agent work.

Evidence model

Evidence Reports are the trust object.

Runtime details matter because they produce something reviewers can use. Evidence Reports make AgentFoundry legible to engineering leaders, reviewers, and accountable owners.

Intent and scope.

Plan and execution summary.

Changed files and systems.

Validation results and failures.

Risks, unresolved questions, approval status, and handoff notes.

Different from generic coding tools

AgentFoundry is governed engineering execution, not another assistant.

Coding assistants help individuals write code. AgentFoundry coordinates bounded engineering work through validation, governance, evidence, approval, and handoff for organizations.

Not a generic workspace.

Not a chatbot.

Not only code generation.

Not a no-code app builder.

A governed engineering work system.

Stack relationship

AgentFoundry is sibling to Lexopedia, not downstream brand clutter.

Lexopedia handles knowledge work: reason, research, analyze, create, decide. AgentFoundry handles engineering work: plan, execute, verify, govern, handoff. Labs provides evaluation and evidence, while DeepBrainz-R remains the model infrastructure behind deeper workflows.

Lexopedia = knowledge work.

AgentFoundry = engineering work.

Labs = evidence.

DeepBrainz-R = infrastructure.

Explore next