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Operate and improve

See which agents are reliable - and which are slow

See whether an agent finishes dependably and how long typical and slow runs take.

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Captured from the product Demo workspace
Compare completion rate, typical latency, and slow-tail latency by agent.

What this changes for your team.

Per-agent metrics calculate terminal outcomes and accepted-to-terminal duration over the selected window. Median and 95th-percentile latency expose both ordinary performance and the slow tail without implying causal diagnosis on their own.

How it works in practice.

  1. 01

    Select a reporting window and an agent with completed work in that period.

  2. 02

    Compute success rate from recorded terminal states under the monitor's outcome definition.

  3. 03

    Compare p50 and p95 accepted-to-terminal duration to spot slow-tail movement.

What you can plan around.

The behaviour you can design against, stated concretely.

Accepted time and terminal time are persisted lifecycle timestamps.

Percentiles are derived from runs in the selected, authorized dataset.

Queued and approval-waiting time are included because the metric begins at acceptance, not worker start.

Complete the operating path

View all capabilities
Operate and improveSpot backlogs before work stalls

Distinguish a healthy burst from work that is accumulating faster than workers clear it.

Operate and improveOperate from one monitor room

Move from an individual run to fleet health and recurring failure patterns without changing tools.

Run it reliablyNever lose track of accepted work

Give every accepted unit of work a durable identity and history before execution begins.

Bring one real process

See how Yekar.AI fits the way you work.

Start with a job your team already owns, plus the tools and decisions around it.

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