Improve AI
agent speed.

Find the process bottleneck slowing your coding agents and fix it first. Check AI agent speed every night.

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NWeb app team · last nightly run

Example data

Agents running at

52%of full speed

  • Coding−7.0%
  • Code review−7.8%
  • Process automation−8.8%
  • Product roadmapFix first−13.4%
  • Cost and tokens−10.7%
01The checks

Agent readiness assessment across five areas.

The agent readiness assessment runs 25 checks: coding, review, automation, roadmap context, and cost. AI agent speed here means how fast work moves through the agent-centered process. Model latency is a separate problem with a separate fix.

01

Coding

−7.0%

How much of the typing the AI actually does

  • Agent fluency−1.0%
    We read
    Days in the last 8 weeks with at least one agent session
    To fix
    Use AI every day, not only on the tickets that look hard
  • Session depth−1.3%
    We read
    Median minutes an agent runs before a person takes over
    To fix
    Let a session finish a task before you step in
  • Delegation size−1.1%
    We read
    Share of agent work that is a whole task rather than a fragment
    To fix
    Give agents whole tasks, not small pieces
  • Prompt quality−1.9%
    We read
    Share of prompts that include a reason and an expected result
    To fix
    Put the reason and the expected result in the prompt, as you would when asking a person
  • Agentic autonomy−1.8%
    We read
    Tasks an agent carried end to end, from ticket to open pull request
    To fix
    One agent-owned task a week, start to finish
02

Code review

−7.8%

How quickly finished work gets checked and released

  • Review cycle time−1.3%
    We read
    Hours from ready for review to the first human decision
    To fix
    Measure the first response, not the merge
  • Queue honesty−1.4%
    We read
    Share of approvals given without a file being opened
    To fix
    Stop clearing the queue by skipping the review
  • Agent first pass−1.7%
    We read
    Share of pull requests reviewed by an agent before a human sees them
    To fix
    Let an agent review anything over 400 lines first
  • Change size−1.6%
    We read
    Median lines per pull request arriving at review
    To fix
    Split agent output before it reaches a person
  • Post-merge rework−1.8%
    We read
    Share of merges followed by a fix within 48 hours
    To fix
    Measure what review missed, not only what it found
03

Process automation

−8.8%

How much routine work still needs a person

  • CI self-healing−1.6%
    We read
    Share of failed pipelines fixed without a person committing
    To fix
    Let an agent try to fix every failed build first
  • Test generation−1.8%
    We read
    Share of new code arriving with tests written alongside it
    To fix
    Make tests part of the same agent run, not a follow-up ticket
  • Documentation upkeep−2.1%
    We read
    Share of shipped changes with docs updated in the same week
    To fix
    Update the documentation from the diff when the change merges
  • Release automation−1.4%
    We read
    Manual steps left between a merge and production
    To fix
    Remove the two steps a person still clicks
  • Incident triage−1.9%
    We read
    Share of alerts that arrive with a suggested cause attached
    To fix
    Let the agent suggest a cause before a person opens the alert
04

Product roadmap

−13.4%

How clearly teams are told what to build, and why

  • Ticket clarity−2.6%
    We read
    Share of tickets a reader can act on without asking a question
    To fix
    Give every ticket a reason and an expected result before work starts
  • Epic decomposition−2.9%
    We read
    Share of epics broken into work an agent can start on
    To fix
    Cut epics down to what one agent run can finish
  • Roadmap coverage−2.2%
    We read
    Share of merged work traceable to a stated objective
    To fix
    Attach every ticket to an objective, or stop funding it
  • Objective context−3.0%
    We read
    Share of objectives whose record answers the ten context questions, from the problem and the customer to the owner and the next review
    To fix
    Write down why each objective exists, not only what it delivers
  • Spec readiness−2.7%
    We read
    Share of work that started with the spec already agreed
    To fix
    Agree the spec before the sprint, not during it
05

Cost and tokens

−10.7%

What the AI costs, and whether anyone asked for it

  • Model routing−1.9%
    We read
    Share of tasks running on the cheapest model that can do them
    To fix
    Route by task, not by habit
  • Spend per merged change−2.1%
    We read
    Dollars of tokens spent on each merged pull request
    To fix
    Measure cost per change, not per person
  • Off-roadmap spend−2.7%
    We read
    Share of token spend with no stated goal behind it
    To fix
    Fix question 04 and this one improves by itself
  • Rerun waste−2.2%
    We read
    Share of spend on repeated runs of the same failing task
    To fix
    Stop paying to run the same failing prompt again
  • Budget visibility−1.8%
    We read
    Teams that can see their own spend before the invoice arrives
    To fix
    Give every team its number weekly
Custom

Add your own checks

Any standard your team holds becomes a check. Every rule, severity and threshold is yours to change.

  • PRs over 400 lines need two reviewersReview
  • Every ticket links to an objectiveRoadmap
  • No team spends over $5k a week on tokensAI cost
  • Add a check
02The findings

AI agent speed findings, with evidence.

Every finding in the agent readiness assessment has a location and an owner. Fix the largest constraint on AI agent speed first.

NWeb app team · findings holding agents back
Open findings124
High severity4
Resolved78

  • Objective with no stated problem, customer or owner

    Roadmap · ENG-2884 · AI Analytics Integrations · −3.0%

  • Token spend with no ticket behind it

    AI cost · Web app team · last 7 days · −2.7%

  • Change shipped, documentation left stale

    Automation · navigaracom/vision · PR #2118 · −2.1%

  • Agent task handed back before the pull request

    Coding · navigaracom/vision · ENG-3012 · −1.8%

  • High-risk merge fixed again within 48 hours

    Review · navigaracom/vision · PR #2004 · −1.8%

03How it works

Agent readiness assessment, checked every night.

The agent readiness assessment reviews 25 checks across coding, review, automation, roadmap context and cost, with a next step for each team.

01

Connect

Read-only setup for your agent readiness assessment.

02

Check

25 checks run every night, across every team.

03

Fix

Fix the process constraint on AI agent speed.

Agent readiness assessment findingsOpenIgnoredResolved
04The payoff

Review AI agent speed after each fix.

In the example, the readiness score for AI agent speed is 52%. Fix the largest bottleneck, then compare the next assessment with what shipped and how fast it got reviewed. A resolved check fixes a process issue; delivery shows whether speed followed.

NWeb app team · where the other 48% goes

Example data

Today 52%Full speed 100%
Speed you already haveRoadmap+13.4AI cost+10.7Automation+8.8Review+7.8Coding+7.0

AI agent speed and readiness questions.

What does AI agent speed mean on this page?

How fast work moves through the process around your coding agents: review, automation, task context, and cost. The agent readiness assessment checks those conditions. Tokens per second and model latency are outside its scope.

How does an agent readiness assessment find a bottleneck?

The agent readiness assessment runs 25 checks across five areas every night and links each finding to the ticket, pull request, or spend behind it. The area costing the most in AI agent speed rises to the top, along with the change the team can make.

Does fixing a check guarantee higher AI agent speed?

No. A resolved check fixes one part of the process. Run the next agent readiness assessment and compare review and delivery results, because task scope, the codebase, and your quality bar also affect AI agent speed.

Can we change the agent readiness assessment rules?

Yes. Every rule, severity, and threshold in the agent readiness assessment is yours, and you can add checks such as “PRs over 400 lines need two reviewers.” Raise AI agent speed within those standards, keeping reviews and tests intact.

How often should we review AI agent speed?

Checks run every night. Pick one finding per sprint, make the change, and read the next agent readiness assessment against what shipped.

What do we connect for an agent readiness assessment?

Read-only access to your repositories, tracker, and AI spend sources. The first run uses history you already have. Start the AI agent speed review with one team and its largest bottleneck.

→Start here

See what slows
your agents.

We run the agent readiness assessment on your data and walk you through the findings that cost the most in AI agent speed.