AI-agent visibility for engineering leaders

See what your AI budget is actually buying.

Connect agent spend to shipped work, repeated effort, and reusable knowledge across every team and tool.

Bring one workflow. Leave with a report of what your current dashboards miss.

Engineering agent report
Last 30 days
Illustrative team report · work, intervention, and alignment
Agent work128.4hhours building
Human in the loop18.7h6.9× agent leverage
On-track work84%within approved direction
WorkflowWork mixHumanAlignment
Authentication migrationIdentity
2.1h
On track
CI sandbox setupDev productivity
9.4h
Needs review4 repeats
Payments servicePlatform
3.2h
On track
Agent workHuman intervention
Team scale

30+ engineers

Enough repeated work and handoffs for shared memory to matter.

Tool surface

Multiple AI tools

A company-level layer keeps knowledge from staying trapped in one agent.

Environment

Fresh sandboxes

CI runners and containers should not rebuild context every run.

Organization

Cross-team work

Decisions and failed paths need to travel across people, repos, and projects.

The visibility gap

You can see what shipped, but not how the team got there.

License reports show seats. Model dashboards show usage. Neither tells an engineering leader which workflows are improving, which lessons are being reused, or where the team is paying to solve the same problem again.

Usage is not learning

More sessions do not show whether the organization is building durable capability.

Spend lacks context

Token totals cannot explain what work they produced or why a workflow stays expensive.

Good practice stays invisible

Leaders cannot see what strong teams do differently or make it available elsewhere.

How XTrace works

Observe. Distill. Enforce.

Agent sessions contain decisions, failed paths, and repeatable procedures. XTrace observes what happened, distills durable knowledge from the noise, keeps lineage to the session that taught it, and serves it back when the next authorized person or agent needs it.

Observe

Record session activity, tool use, procedures used, token cost, and what shipped.

Distill

Turn a failure into a gotcha, a fix into a procedure, and a repeated choice into a principle.

Share

Move approved procedures and lessons from agent to agent and teammate to teammate.

Recall

Serve context at session start, on demand, or when a known failure appears.

Enforce

Apply the rulebook before an action runs, with advise, gate, or block controls.

Fact

The repository uses this authentication pattern.

Procedure

Follow these steps before changing the memory store.

Episode

This migration failed because the backfill order was wrong.

Artifact

This approved runbook is the current source of truth.

When memory reaches the agent
At session start

Sync a short, relevant rulebook so the agent begins with team context.

Baseline context
Before an action runs

Match a pending action to a constraint or procedure before it executes.

Advise · Gate · Block
When something fails

Match the failure to a known lesson and surface the prior fix when it matters.

Reactive guidance
What changes

Turn agent activity into a capability the team can keep.

Preserve engineering scar tissue

Keep the reasoning, caveats, and failed approaches behind important work.

Make AI work multiplayer

Let the next engineer and agent start from what the team already learned.

Reduce late review surprises

Surface procedures and constraints before the same mistake reaches review again.

Evaluate adoption with evidence

See what is reused, where the team starts over, and what deserves a broader rollout.

Enterprise controls

Share useful context without sharing everything.

Memory only helps when the right people and agents can use it. XTrace keeps knowledge source linked and lets administrators control how it is organized, shared, and maintained.

Scoped access by person, team, brain, and workspace
Viewer, contributor, and administrator roles
Source-linked answers, procedures, and artifacts
Separate workspaces for teams and workflows
SSO and SAML support for enterprise access
Guided onboarding for approved integrations
Procedure lineageApproved for recall
SourceAuthentication reviewDecision and failed migration path
ProcedureMigration checksOwner: Platform Engineering
RecallRelevant file touchedGuidance shown before action
Identity workspacePayments workspaceContributors onlySource retained
A focused first step

Bring one workflow. Leave with a clear test plan.

The working session is not a generic product tour. We will map where context disappears, who and what needs access, which controls matter, and what a useful pilot would need to prove.

1Choose the workflow
2Map the memory gap
3Define controls and success
4Scope the pilot
Schedule with AustinOpen in new tab ↗
Loading calendar…
FAQ

Questions engineering leaders ask.

Bring the integration and governance questions specific to your rollout to the working session.

Does XTrace replace our coding agents or developer tools?

No. XTrace sits across the tools your team already uses. It keeps useful knowledge from being trapped in one agent, session, or repository. Integration scope is confirmed during the working session.

How is this different from a wiki or enterprise search?

A wiki stores documents. Search retrieves information after someone asks. XTrace focuses on decisions, procedures, failed paths, and context produced during agent work, then connects that knowledge to the workflow where it should be reused.

Is this employee monitoring?

XTrace makes agent work, shared lessons, and governance visible at the workflow and team level. It is not positioned as an employee productivity score. Access and sharing are controlled by the organization.

Can we control what becomes shared memory?

Yes. Knowledge stays tied to its source and can be organized and shared by person, team, brain, or workspace, with viewer, contributor, and administrator roles.

Does it work across multiple agents and model vendors?

That is the core use case. XTrace provides a company-level memory layer so useful context can travel across authorized tools and workflows. Confirm the exact integration set during the working session.

What happens after the session?

If there is a strong fit, we define the team, workflow, controls, success metric, decision process, and scope for a 6 to 8 week enterprise pilot. There is no commitment to proceed.

What is your AI spend actually producing?

Bring the workflow you cannot explain from a dashboard today. We will map what evidence already exists, what is missing, and what a leader should be able to see.

Book an agent workflow review