AgentPing for platform and AI engineering

Run a fleet of agents
like production infrastructure.

When dozens of agents run on schedules and serve real traffic, you need observability built around the run, not the request. AgentPing gives platform teams incidents, schedule freshness, cost guardrails and run-level detail across the whole fleet, beside the stack you already run.

Track your first AI workflow
support-triage · schedulelive
support-triage missed its 14:00 run paged on-call · last ok 13:00 · 247 runs clean before

Production observability for AI agents.

One record per run, across the fleet. Reliability and incidents first, with cost attribution, output quality and a hard safety ceiling on top.

Incidents and schedule freshness

Missed runs, failures and stalls raise incidents and page on-call, each linked to run-level detail.

support-triage · schedulelive
support-triage missed its 14:00 run paged on-call · last ok 13:00 · 247 runs clean before

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Cost attribution at scale

Cost per agent, model and run across the fleet, priced server-side and cache aware, with anomaly alerts.

spend · this month↑ on budget
$5,382
spent
$0.094
cost / successful run
content-writer$2,189
research-agent$1,474
support-triage$685
email-classifier$262

Explore Spend →

Output quality signals

Deterministic checks and judge scoring on production output, so regressions surface as a trend, not a ticket.

summariser · judge score↓ 4.2 to 3.8
  • cites a source pass
  • answers the question pass
  • stays on policy fail

Explore Verify →

Running autonomous agents without a human watching? Guard puts a hard daily spend cap and step ceiling on every run, so a runaway loop is blocked before it burns the budget.

How is this different from our APM or logs?
APM and logs are built around requests and exceptions. AgentPing is built around the agent run: one record per run carrying cost, status, latency, tools, retries and quality. It sits beside your existing stack and answers the questions an AI fleet raises that request-level tools were not designed for.
Does it scale to a fleet of agents?
Yes. The ingest tier is built for high-volume run and event traffic, identifiers are client-generated and idempotent, and the SDK never blocks or crashes your code, with a hard timeout and a bounded local queue. Agents, runs and incidents roll up by team so a large fleet stays legible.
How does on-call work?
Define expected schedules and thresholds; missed runs, failures, stalls and cost anomalies raise incidents and page the channel you watch, over Slack, email or webhook. Each incident links to the run-level detail so the responder starts with context, not a blank log search.
Can we put a hard ceiling on spend and loops?
Guard is a top-of-run safety check: set a daily spend cap and a step ceiling, and a runaway loop is blocked before it burns the budget. It is the guardrail for autonomous agents running without a human watching.

Give your agent fleet a control plane.

Point one agent at AgentPing and see run-level cost, status and quality. Add schedules, thresholds and guardrails as you roll out the fleet.

Track your first AI workflow

For automation agencies → For product teams → All features →