How is DevClocked different from Waydev?

Waydev reads your Git history and infers which commits were AI. That's the artifact after it lands. We sit in the work session and capture it as it happens — real hours, every agent run, the tokens — including everything that never became a commit. They measure the code; we measure the working. And they're top-down for managers, while we land bottom-up with the developer — which is the data they can't get.

How is DevClocked different from Tokscale?

Tokscale tells you what you spent. We tell you what you shipped for it. Measuring spend is the easy half and it's free; tying it to real human hours and shipped output — the leverage — is the hard half, and the part that matters.

How is DevClocked different from Jellyfish or LinearB?

Same data limit as Waydev — they're built on Git and pull requests about humans, with AI bolted on as adoption stats. We're built on the work session itself, captured at the source.

How is DevClocked different from Langfuse?

Different end of the pipe. Langfuse traces what the model does, for people building agents. We measure the output and cost of people using them to ship software.

What is Agentic Engineering Intelligence?

Agentic Engineering Intelligence measures the combined work of human engineers and their AI agents — capturing where time actually goes (real coding versus config), every agent run and token cost, at the source, across all repos, and tying it to shipped output — so organisations can see what their AI-assisted engineering produces and what it costs.

For Developers For Teams For Agencies

Work layer map

How agentic engineering becomes measurable.

1SessionA continuous window of engineering activity

2Work BlocksProject-attributed units inside the session

3Time SliceCoding, config, review, and debug across blocks

4Agents + tokensRuns, cost, and contribution captured per block

5Shipped outputWork tied back to repos, features, and delivery

Why now

Software is no longer shipped by humans alone. Agent runs, token costs, orchestration time, and config toil are now part of engineering output.

Different from Git analytics

Git-based analytics read artifacts after they land. Agentic Engineering Intelligence captures the work session before, during, and around the commit.

Different from token counters

Token counters show spend. Agentic Engineering Intelligence shows what the spend produced, what human time it required, and whether leverage improved.

The work layer beneath the artifact.

A practical category for teams that need to understand humans, agents, costs, and shipped output together.

Work Block

The project-attributed unit inside a session: which client, repo, feature, or task the work belongs to before Time Slice analyses it.

Time Slice

The analytical layer over Work Blocks: coding vs config, plumbing, review, and debug separated at the source instead of guessed from commits.

Work-session capture

The source layer: IDE, terminal, browser, Mac app, and agent sessions captured while the work happens.

Multi-agent, multi-repo view

One picture of concurrent humans and agents across the repos that make up a real product initiative.

Attribution

What shipped, who or what helped ship it, and which project or team it belongs to.

Leverage

The ratio between human hours invested and total shipped output from humans plus agents.

Token economics

Token cost by project, session, and agent tied to output rather than viewed as a disconnected bill.

Privacy-first

Metadata-level capture designed to measure work without screenshots, keystrokes, or surveillance.

One work layer, three launch funnels.

Developers prove their leverage. Teams measure the org. Agencies make human and agent work billable.

For Developers

See your real day to the minute — coding vs config, agents, repos. Prove your leverage.

Open page

For Teams

Measure the real work your org ships across humans and agents — the part Git can't see.

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For Agencies

Make multi-developer, agent-assisted client work billable and defensible.

Open page

Category FAQ

The short answers for teams comparing Git analytics, token counters, and agent observability.