AI agents
Agents that act within limits you define, not limits they guess at.
Where a task involves several steps and some judgement — researching, drafting, checking against multiple sources — an AI agent can help, provided it is given explicit tools, budgets, and approval gates rather than open-ended autonomy.
Problems this solves
Where an agent is more appropriate than a fixed workflow
The task requires checking several sources and combining the result, not a single fixed lookup.
The right next step depends on what earlier steps found, rather than following one fixed sequence.
A skilled person currently does this manually, in a way that is well-understood but time-consuming.
The task benefits from drafting or summarising, with a human reviewing before anything is finalised.
A fixed rule-based workflow would need constant updates to handle the task's natural variability.
What we deliver
A defined tool set
The agent can only take actions we explicitly grant it — no open-ended system access.
Budgets and usage limits
Cost ceilings and alerts so an agent cannot run unexpectedly expensive loops unnoticed.
Approval gates for consequential actions
Anything that writes data, sends a message, or spends money is reviewed before it happens, unless explicitly agreed otherwise.
Full observability
A traceable log of what the agent did, which tools it used, and why, for every run.
How the engagement works
A controlled path from task to trusted agent
- 01
Define
Specify the task, the tools the agent needs, and where human approval is required.
- 02
Build with guardrails
Implement budgets, tool limits, and approval gates alongside the agent's core logic.
- 03
Pilot under supervision
Run the agent on real tasks with close human review before reducing oversight.
- 04
Scale deliberately
Expand scope only once accuracy and safety are demonstrated, not assumed.
Relevant technologies
Chosen for control and observability
Agents
- LLM APIs
- Tool-calling frameworks
Controls
- Budget enforcement
- Approval gates
- Audit logging
Infrastructure
- Docker
- PostgreSQL
Security and operational considerations
Autonomy with limits, not autonomy without them
Explicit allow-lists for which tools and systems an agent can access.
Hard budget ceilings, with alerts before limits are reached.
Human approval before any sensitive, irreversible, or costly action.
Full audit logs of every tool call an agent makes.
A kill switch to halt an agent's activity immediately if it behaves unexpectedly.
Related case studies
Case studies in progress
FAQ
Common questions
Considering an AI agent for a specific task?
Start by defining the task narrowly — the guardrails follow naturally from a clear scope.