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See what your agents did, and decide what happens next

See reported activity, investigate incidents, review outcomes, and add human approval to integrated actions.

TOOLCAISE / WORKBENCHILLUSTRATIVE PRODUCT VIEW
Overview

Today’s runs

Example data
12reported runs
3awaiting review
Reported activity / example
AGENT / CONTEXTREPORTED STATENEXT STEP
Market researchRun completedReview outcome
Weekly reportingRun completedReview outcome
Catalog monitorRun in progressView steps

A reported completion is the start of review, not independent proof of a result.

Interactive illustration of supported workflows. Sample counts and activity are not live customer data.
5

ways to connect

Python and Node.js SDKs, HTTP, OpenTelemetry, or Toolcaise Connect

4

supervision layers

Activity, incidents, review, and control

0

inbound ports required

Agents and Connect report over outbound HTTPS

The four parts of the product

What an agent did, what went wrong, whether the result was any good, and whether it may take the next step. Each card opens the detail.

Every run, step and cost

Bring agent heartbeats, run histories, step traces, and reported usage into one operational view.

How it works →

Catch work that never happened

Investigate stale heartbeats, overdue schedules, stalled runs, and repeated failures in an agent supervision workspace.

How it works →

Review what the agent produced

Keep agent-reported success separate from human acceptance with an explicit outcome review record.

How it works →

Approve specific actions

Add human approval to specific agent actions with digest-bound, single-use authorization and cooperative SDK checkpoints.

How it works →

What each part looks like in practice

Every run, step and cost

Bring agent heartbeats, run histories, step traces, and reported usage into one operational view.

  • Follow a run from start to finish
  • Keep usage beside the work
Explore agent monitoring →

Catch work that never happened

Investigate stale heartbeats, overdue schedules, stalled runs, and repeated failures in an agent supervision workspace.

  • Separate health from delivery
  • Acknowledge with context
Explore incident management →

Review what the agent produced

Keep agent-reported success separate from human acceptance with an explicit outcome review record.

  • Review the actual deliverable
  • Record a human decision
Explore outcome review →

Approve specific actions

Add human approval to specific agent actions with digest-bound, single-use authorization and cooperative SDK checkpoints.

  • Bind the request to the action
  • Consume authorization once
Explore action approvals →

What each check tells you, and what it doesn’t

Give every decision the right evidence. Keep execution, quality, and permission separate.

  1. Is the agent online?

    Inspect
    Recent heartbeat
    Does not prove
    That the scheduled work happened
  2. Did the run finish?

    Inspect
    Reported run and step history
    Does not prove
    That the result is useful
  3. Is the result acceptable?

    Inspect
    Artifact and human outcome review
    Does not prove
    That a later external action succeeded
  4. May this action proceed?

    Inspect
    The exact request and approval
    Does not prove
    That the destination received it
Read the guide to approval and verification →

Your agents keep running where they are

Instrument an existing script, cloud worker, or workflow with the Python or Node.js SDK, the HTTP contract, or the OpenTelemetry traces it already emits, or run it under Toolcaise Connect.

No model-provider key, inbound port, or replacement agent runtime is required. You choose which operational metadata to report.

Monitoring does not automatically confer control. Add compatible checkpoints and action guards wherever you need a human decision, or run the agent under Toolcaise Connect to enforce cost, token and runtime limits.

Frequently asked questions

What is Toolcaise?

Toolcaise is a supervision workspace for AI agents you already run. It records each run and what it cost, opens an incident when scheduled work goes missing or stalls, lets a person accept or reject what an agent produced, and holds consequential actions until someone approves them. With Toolcaise Connect it can also stop a run at a cost, token or runtime limit.

Do I have to move my agents into Toolcaise?

No. Your agents keep running in your own infrastructure and report over outbound HTTPS through an SDK, the HTTP API, OpenTelemetry or Toolcaise Connect. Toolcaise never runs your code and never needs your model-provider keys.

How long does it take to connect the first agent?

For an agent you can edit, adding the SDK and wrapping the main loop in a run is a short change. For a command you cannot edit, Toolcaise Connect wraps it without code changes. The setup screen confirms the first report as soon as it arrives.

Which parts can actually stop an agent?

A run wrapped by Toolcaise Connect is stopped, with every process it started, at the limits you set or from the dashboard. Through the SDKs, pause, cancel and approvals are cooperative and apply at checkpoints in your code. OpenTelemetry only observes. The integrations page lists exactly what each path can enforce.

Connect your first agent

Create a free workspace, connect the agent you already run, and watch its next run arrive. Free covers 3 agents, with no card required.