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AgentMon Start

Your agents are doing the work.
Know whether it’s good work.

See how much agent output gets reworked, which agents finish their tasks, what each successful task costs, and where time and tokens disappear.

Free for 3 seats, 6 months

Agent engineering scorecard

Last 30 days · sample data

  • Rework rate

    18%

    ▲ 3 pts vs last month

  • Tasks completed

    72%

    ▲ 6 pts

  • Correct context

    91%

    of sessions

  • Cost / success

    $3.40

    ▼ $0.85

  • Wasted iterations

    11%

    of agent time

  • Merged PRs / week

    +34%

    since rollout

  • Review wait

    2.4 d

    bottleneck: payments

  • Longest unattended

    5h 40m

    0 progress lost

  • 81%

    of leaders expect AI agents to be integrated into their AI strategy within 12-18 months.

    Learn more

  • 97%

    of organizations rely on multiple tools and services to manage their cloud environments.

    Learn more

  • 989B

    AI/ML transactions observed by Zscaler in 2025, up 91% year over year.

    Learn more

What management needs to know

  • How much agent work needs correction?

    Code quality and rework

  • Which agents finish their assigned work successfully?

    Agent reliability and task completion

  • Are agents using the correct repository, specs and architectural decisions?

    Context and knowledge

  • What does each successful task actually cost?

    Cost and token efficiency

  • How much time is lost to retries, errors and failed approaches?

    Debugging and wasted iterations

  • Does increased agent usage improve outcomes, and which harness is best for which task?

    Productivity measurement and harness comparison

  • Are agents creating integration and review bottlenecks?

    Team coordination

  • Can agents work for hours without losing progress or needing repeated help?

    Long-running agent management

Your agents work fast.
Now measure what they deliver.

Trust the output

Know how good agent work is, and which agents finish it.

Code quality & rework

How much agent work ships as-is, and how much a human rewrites.

AgentMon follows each agent change through review and the commits after it, showing what was accepted, lightly edited or substantially rewritten, by agent, repo and task type.

Rework by agentsample data
backend-agent
frontend-agent
test-writer
migration-agent
Accepted as-isMinor editsRewritten

Reliability & task completion

Which agents finish the job, and where the others drop out.

Every assigned task is tracked from start to merge. Completion rates by agent, model and task type show where work stalls: abandoned mid-run, failing CI, or rejected in review.

Task completion funnelsample data
Assigned
412
Ran to end
367
Passed CI
331
Merged
297

Biggest drop-off: 45 tasks abandoned mid-run, 31 of them by migration-agent on very large repos.

Right inputs, real cost

Confirm agents worked from the right knowledge, and what each success cost.

Context & knowledge

Did the agent use the right repo, spec and architectural decisions?

Most bad agent output starts with bad inputs. AgentMon records which branch, files, specifications and decision records each session read, and flags sessions working from stale or missing context.

Context trace · session 8f2csample data
  • ✓ repo payments-svc @ main (a41e9d0)
  • ✓ spec PAY-214 refund flow, v3 (current)
  • ! spec PAY-198 ledger API, v1 (superseded)
  • ✕ adr ADR-017 idempotent writes: not loaded

Change touches ledger writes without ADR-017 in context. Flagged for review before merge.

Cost & token efficiency

Cost per successful task, not cost per API call.

Spend per agent, model and team divided by tasks that actually merged, including tokens burned on attempts that never shipped. Set budgets and get alerted before limits are hit.

Cost per successful tasksample data
AgentSpendMerged$ / success
test-writer$212118$1.80
backend-agent$34196$3.55
frontend-agent$26861$4.39
migration-agent$18922$8.59

Waste finder: 38% of migration-agent spend went to a premium model on tasks a smaller model completes.

Cut the waste

Find lost hours, prove agents move the numbers that matter, and route each task to the harness that does it best.

Debugging & wasted iterations

The hours lost to retries, errors and approaches that went nowhere.

Reasoning-loop detection catches agents retrying the same failed fix. Errors are grouped by root cause and sent to Slack or PagerDuty.

Where agent time went this weeksample data
Productive · 71%Retries · 12%Tool & API errors · 9%Abandoned approaches · 8%

LOOPfrontend-agent retried the same failing snapshot test 14 times in 26 minutes. Stopped and routed to #web-oncall.

Productivity measurement

Does more agent usage mean better engineering outcomes?

Put agent activity next to the delivery metrics you already report: merged changes, lead time, escaped defects. See per team whether rising usage is moving them.

Agent usage vs. merged changessample data
Usage rises, output flattens
Agent sessionsMerged changes12 weeks

Harness comparison

Claude Code, Codex, Kiro or Cursor? See which harness is most productive for each kind of task.

Teams rarely run just one coding agent. AgentMon Start builds this report for you automatically: it compares every harness on your own repos and task types, held to the same review standard, and shows the winner for each kind of work.

AgentMon Start/ Productivity / Harness comparison Last 30 daysAll reposAuto-generated

Recommendation for your profile is:

1,284 merged tasks measured · sample data

Task typeBest harnessMerged as-isCost / merged taskTasks
Bug fixClaude Code81%$1.90312
New feature from specKiro74%$4.20188
RefactorCodex77%$2.95201
Test writingCodex88%$1.30297
Large migrationClaude Code64%$7.1064
Docs & configCursor89%$0.70222

ROUTERouting each task type to its best harness would cut cost per merged task by 19% at current volumes.

Built by AgentMon Start from your agents' OpenTelemetry traces and review outcomes. Updated hourly.

Scale without friction

Keep teams unblocked, and let agents run for hours.

Team coordination

The review and integration bottlenecks agents create downstream.

Agents open changes faster than people can review them. See review queues by team and reviewer, agents colliding on the same files, and merge conflicts.

Open changes awaiting reviewsample data
payments4.1 d
web1.6 d
platform0.9 d
Agent-authoredHuman-authoredright: median wait

3 agents edited ledger/write.go in parallel this week: 5 merge conflicts.

Long-running agents

Let agents run for hours, and know they kept their progress.

See the full session timeline: checkpoints reached, context resets, stalls and every time a human had to step in. Measure how long agents work unattended.

migration-agent · 6h 12msample data
0h2h4h6h
  • 4 checkpoints saved; context reset at 3h 05m resumed with no work lost
  • 1 human intervention: schema change approval (6 min)
  • Completed: 212 files migrated, CI green

Answers you can defend, built on telemetry you can trust

One dashboard for everything

Every agent in a single view: status, model, cost, live activity, and who spawned what.

Works with your stack

OpenTelemetry-native. No proprietary SDKs, no lock-in. Alerts to Slack or PagerDuty.

Automatic secret redaction

API keys and tokens stripped from telemetry before it is stored. Risky prompts flagged as they happen.

Audit trail + SIEM export

A tamper-evident record of every agent action, forwarded to your tools.

Walk into your next review with numbers, not anecdotes

Connect your agents in minutes and get rework, completion, cost-per-task and bottleneck data from day one.

Free for 3 seats, 6 months