Observability for coding agents

The management layer for coding agents.

See what coding agents are doing across your org, what they cost, and where quality and risk diverge.

Get one operational view across quality, cost, governance, and usage instead of stitching together vendor dashboards.

See the product through

Designed for engineering leaders who need operational clarity without micromanaging individuals.

Works across Claude, Codex, Cursor, Copilot, and more.

Org work

Quality, spend, and governance in one operating view

Quality score

74

Monthly spend

$18.4k

Policy aligned

87%

Signal mix

Last 30 days
Quality52%

Break down recurring risks and stronger operating patterns by team, project, model, or agent.

Cost63%

Track spend concentration, spike days, and efficiency shifts before AI coding spend becomes opaque.

Governance74%

See permission modes, risky artifacts, and policy posture next to the rest of the operating picture.

Quality

Break down recurring risks and stronger operating patterns by team, project, model, or agent.

Cost

Track spend concentration, spike days, and efficiency shifts before AI coding spend becomes opaque.

Governance

See permission modes, risky artifacts, and policy posture next to the rest of the operating picture.

Quality signals by team, model, or project

See where process quality and anti-pattern rates diverge across the org.

Spend trends and top cost drivers

Understand what is driving AI coding spend instead of only seeing aggregate totals.

Governance posture in the same layer

Policy risk, permissions, and risky artifacts stay connected to actual usage.

Cross-tool visibility

Diff gives one operational surface across tools instead of siloed vendor analytics.

Quality

See recurring patterns, breakdowns, and risk indicators behind coding-agent usage.

Cost

Track spend, spikes, and concentration before AI coding budgets become opaque.

Governance

Understand permissions, risky artifacts, and policy posture alongside operational usage.

Cross-tool

Compare Claude, Codex, Cursor, Copilot, and more in one operating layer.

For engineers

See your sessions, patterns, and setup in one place.

Diff gives individual engineers a view into their coding-agent work across sessions, tools, and configurations so they can understand what is working and what is not.

Track personal session patterns over time instead of relying on memory.

See which workflows, tags, and habits recur across your work.

Understand the configuration and setup context behind the sessions.

For Engineers

Personal signals

A clearer personal view of coding-agent work

Active days

19

Pattern tags

12

Work items

6

Signal mix

Last 30 days
Personal trends52%

See what has been increasing, stabilizing, or falling off in your own work.

Workflow context63%

Sessions are more useful when viewed with their surrounding setup and process cues.

Useful before procurement74%

The product should make sense for an engineer before any org-wide rollout happens.

Personal trends

See what has been increasing, stabilizing, or falling off in your own work.

Workflow context

Sessions are more useful when viewed with their surrounding setup and process cues.

Useful before procurement

The product should make sense for an engineer before any org-wide rollout happens.

Personal signal, not just admin reporting

The engineer path stays useful even without an org dashboard purchase.

Pattern visibility

Use recurring tags and trends to understand how your sessions are evolving.

For managers

See quality, cost, governance, and usage in one system.

Diff gives engineering leaders a cross-tool operational layer for understanding how coding agents are being used, where outcomes diverge, and where intervention is needed.

Break down quality and risk by team, project, model, or agent.

Track spend and efficiency, not just usage or seat counts.

See governance posture next to the rest of the operating picture.

For Managers

Org overview

One operating view across the coding-agent stack

Tracked teams

12

Top 3 concentration

58%

Risk exposure

9 sessions

Signal mix

Last 30 days
Operational breadth52%

Bring quality, cost, governance, and usage into one shared system instead of separate conversations.

Actionable segmentation63%

Slice by project, team, model, or agent to find where operating differences matter.

Management without surveillance74%

Keep the product focused on system understanding and operational improvement.

Operational breadth

Bring quality, cost, governance, and usage into one shared system instead of separate conversations.

Actionable segmentation

Slice by project, team, model, or agent to find where operating differences matter.

Management without surveillance

Keep the product focused on system understanding and operational improvement.

Break down the org view

Leaders can inspect team, project, model, and agent differences without losing the top-line picture.

Usage plus posture

Adoption metrics become more meaningful when they sit beside cost and governance data.

Also Useful To

Adjacent stakeholders.

Finance

Understand AI coding spend, spike days, and cost concentration by team, model, and workflow.

Security

See permission modes, risky artifacts, and policy-alignment signals alongside actual usage.

EngOps

Normalize cross-tool telemetry and compare setup differences across teams and providers.

Personal proof

A public-safe view of agentic coding activity.

Diff can expose public-safe aggregate activity for engineers without exposing raw transcripts or sensitive project details.

Explore the engineer path

Public profile

@agentic-eric

Aggregate activity only. No raw transcript exposure.

Sessions

148

Streak

12d

Less
More

Trust

Built for visibility, not surveillance.

Diff is designed to help teams understand coding-agent operations without turning engineers into scorecards.

Individual-facing value

The product should be useful to engineers themselves, not only to management.

Controlled visibility

Cross-tool observability works best when access and visibility are intentional rather than indiscriminate.

Public-safe defaults

Where public or shared surfaces exist, they should default to aggregate-safe exposure rather than raw detail.

For Engineers

Start with your own sessions.

See how you work with coding agents across sessions, tools, and setup.

Start free

For Managers

See your org's baseline.

Understand quality, cost, governance, and cross-tool usage in one layer.

Book a demo