UNDERSTAND THE PROBLEM
Begin with the failure you need to prevent.
An agent becomes risky when its goal is broad, its tools are powerful and the team cannot state what completion looks like. The problem is not that the model takes several steps. It is that authority, state and failure behaviour remain implicit.
High-consequence workflows need narrower jobs. Each job should have explicit inputs, allowed tools, completion criteria, budget, timeout, checkpoint and fallback. The model may choose how to progress inside that envelope without deciding the envelope itself.
This changes agent design from 'give the model access and see what happens' to a workflow the organisation can test, observe and operate.
DESIGN THE SYSTEM
Make the operating rules explicit.
A dependable AI system is easier to build when the team can see the decisions, evidence, boundaries and ownership around it. The following principles turn an ambiguous ambition into components that can be implemented and reviewed.
Give the agent a job, not a mission.
A job has a bounded outcome and known stopping condition. A mission invites the model to invent intermediate authority the product never approved.
Expose the smallest useful tool surface
Wrap tools around specific actions, validate arguments and separate read, propose and commit permissions.
Make state durable
Long-running work should survive retries and service failures without repeating consequential actions or losing the reason for a decision.
Place checkpoints before irreversible actions
A deterministic check or human approval belongs before money moves, records change, advice is issued or an external message is sent.
IMPLEMENT IN ORDER
Build the smallest complete loop.
Do not automate every adjacent task at once. Start with one valuable journey, carry it from signal to outcome, and preserve enough evidence to know whether it worked. Expand only after that loop is dependable.
- 01
Draw the existing workflow
List inputs, decisions, systems, handoffs, side effects and the person currently accountable for the result.
- 02
Choose one bounded job
Automate the smallest sequence that creates a useful outcome and still has an obvious owner and fallback.
- 03
Define tool contracts
Give every tool typed arguments, permission rules, idempotency behaviour, error states and a record of what changed.
- 04
Add checkpoints and limits
Set maximum steps, cost, elapsed time and approval points before any action that is difficult to reverse.
- 05
Evaluate full trajectories
Grade whether the agent chose the right path, recovered from failure, used tools correctly and produced the intended outcome.
KNOW WHEN IT WORKS
Measure behaviour, not how impressive the demo looks.
The useful measure is whether the system creates the intended business or product outcome while staying inside its boundary. Review these checks before launch and whenever the model, data, prompt, tools or workflow changes.
Authority
The agent cannot reach tools or actions outside the current job's permission boundary.
Recovery
Retries do not duplicate side effects, and a failed run can resume or hand over with context.
Trajectory
The team can inspect why the agent chose each tool and where it changed state.
Outcome
Success is measured by the completed business task, not the quality of the agent's narration.