Agents with defined authority, and a record of what they did.

AI & Automation · Artificial Intelligence

Systems that take multi-step action against your tools — bounded by explicit permissions, stopped at the decisions that need a person, and auditable afterwards.

The problem with autonomous by default

An agent that can act is an agent that can act wrongly. The engineering question is not whether it can complete the task, but what it is permitted to do, where it must stop, and what happens when it is confidently mistaken halfway through a sequence.

  • A process needs several steps across several systems, and a person currently stitches them together.
  • An agent demo worked, and nobody could say what it would be allowed to touch in production.
  • There is no answer to "what happens if it gets step three wrong".
  • Nothing would record what the agent did, or on what basis.

What we build

An agent with a narrow, explicit tool set, service credentials scoped per action, mandatory checkpoints at consequential decisions, and an activity trail equivalent to what you would expect from a member of staff.

  • A defined tool set — the agent can only do things you have deliberately given it
  • Service identity and permissions scoped per action, not a shared admin credential
  • Human checkpoints at the steps where being wrong is expensive
  • Reversal or compensation paths for actions that can be undone
  • A complete activity trail: what it did, why, and on what evidence
  • Spend, rate and loop limits so a malfunction is bounded

How it runs

Authority is designed before capability. That order is not negotiable.

  1. 01
    Define the authority

    What the agent may read, what it may change, and what it may never do without a person. Written down and signed off.

  2. 02
    Build the tools narrowly

    Each capability is an explicit, permission-scoped tool rather than open access to a system.

  3. 03
    Place the checkpoints

    Consequential steps — payments, external communication, irreversible changes — stop for approval.

  4. 04
    Bound the failure

    Limits on spend, rate and retries mean a malfunctioning sequence stops rather than accelerates.

  5. 05
    Record everything

    The trail captures reasoning and action, so a review after the fact is possible rather than speculative.

What changes once it is running

What a bounded agent changes about multi-step work.

Sequences complete themselves

Multi-system work that needed a person to shepherd it runs to completion without one.

Authority is explicit

What the agent may do is a reviewable artefact rather than an emergent property of its prompt.

Failures stay contained

Limits and checkpoints mean a wrong step is a stopped sequence, not a cascading one.

Actions are reviewable

Every action has a record, which is the minimum bar for letting software act on your behalf.

How an engagement is shaped

Deliberately conservative. We would rather ship a narrow agent that holds than a broad one that surprises you.

01

Authority design

Two weeks defining the permitted action set and the checkpoints with the process and risk owners. This is the deliverable that matters most.

02

Build and shadow

The agent runs in proposal mode against real work — recommending rather than acting — until its decisions are trusted.

03

Release and operate

Authority extended step by step, with monitoring on action volume, checkpoint rate and reversals.

Common questions

The things buyers ask before they commit. If yours is not here, it is a good first question for the assessment.

Is this safe to point at production systems?
With scoped credentials, checkpoints and limits, yes — that is the whole design. Pointed at production with a broad credential and no checkpoints, no. The safety is in the boundary, not in the model.
How is this different from workflow automation?
Workflow automation executes a path you defined. An agent decides the path within limits you defined. Use the workflow when the steps are known — it is cheaper and more predictable. Reserve agents for genuine variability.
What happens when it is confidently wrong?
It stops at a checkpoint, hits a limit, or is caught in review. If none of those would catch it, the step should not have been delegated, and we will say so during authority design.

Describe the sequence somebody currently shepherds.

Several steps, several systems, one person chasing it. We will map what could be delegated and what should not be.