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

AgentMon boosts your AI agents’ productivity, improves the quality of their output, and keeps every agent secure and under your control.

Free for 3 seats, 6 months

Cost and waste reduction

See what each successful task costs and where tokens and time are wasted.

Task success and quality

Measure which tasks finish, pass CI and merge, and how much of the work gets reworked.

Organizational productivity

Know if more agent usage delivers more merged work across teams.

Security enforcement

Spot dangerous behavior and enforce organizational security policies.

Agent inventory and ownership

Discover every agent in use, which model it runs and who is responsible for it.

Audit and compliance

Keep a tamper-evident record of every agent action, ready for auditors and SIEM export.

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

Agent adoption is accelerating

  • 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

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 and prove agents move the numbers that matter.

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

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