Illustrative use case
Customer-service support
An agent drafts replies from the knowledge base and order history; a person sends or edits them.
Solutions
Build AI agents that can reliably do real work
AI agents can now take actions in your systems: reading documents, calling APIs, updating records. That makes them useful, and it makes reliability, permissions and oversight essential. We design, build and test agents with bounded autonomy, so they do the work they are trusted with and nothing more.
When an agent is the right tool
Use an agent for
- Multi-step work that follows recognisable patterns but varies case by case.
- Work that spans several systems and documents.
- High volumes where people spend time gathering, checking and re-keying information.
Don’t use an agent when
- A simple rule, form or integration would do the job more reliably.
- The cost of an error is high and nobody can review the output in time.
- The data or systems the agent needs are not accessible or trustworthy yet.
We will tell you when an agent is the wrong tool.
How we build agents
Your workflow
We start from the business process and the human oversight pattern, not from the technology.
Bounded autonomy
The agent
- Orchestration: steps, agents and deterministic software coordinated so each does what it is best at
- Context and memory: the right information, and only the right information
Guardrails
- Permissions and boundariesLeast privilege by default.
- ApprovalsHigh-impact actions need a person’s approval.
- EvaluationTested against realistic scenarios before release, and re-tested after every model or prompt change.
- Monitoring and traceabilityWhat the agent did and why is recorded, so issues can be investigated.
- SecurityTested for prompt injection, data leakage and tool misuse.
Your systems
Connected through well-defined, permissioned interfaces, including MCP where appropriate.
Deployment and operation: released with controls, then monitored and improved.
Human + AI
The right balance between human judgement and AI execution
Not every task should be automated, and not every automated task should run unsupervised. We set the level of autonomy by the consequence of an error.
Human executes, AI assists
AI drafts, searches or suggests. A person decides and acts.
Typical fit: High-judgement or high-consequence work
Human directs, AI executes
A person sets the task; AI carries it out and reports back.
Typical fit: Well-defined tasks that still need direction
AI executes, human reviews
AI completes the work; a person approves before it takes effect.
Typical fit: Repeatable work where errors are costly
AI executes, human handles exceptions
AI runs the workflow; defined cases escalate to a person.
Typical fit: High-volume work with clear rules and low-cost errors
Example use cases
Illustrative use case
Document workflows
An agent reads incoming documents, extracts fields and files them, escalating anything unclear.
Illustrative use case
Knowledge retrieval
An agent answers internal questions with citations, within each user’s access rights.
Illustrative use case
Software engineering support
Agents triage issues, draft tests and propose changes for engineers to review before merge.
Illustrative use case
Research assistance
An agent gathers and summarises sources for an analyst, who checks and owns the conclusions.
Illustrative use case
Operational workflows
An agent coordinates steps across systems, with approvals on any action that changes records.
Illustrative use case
Sales operations
An agent keeps CRM records current and prepares account briefs before meetings.
Frequently asked questions
What is an AI agent?
An AI agent is software that can take actions in your systems, such as reading documents, calling APIs and updating records, to complete multi-step work.
What does "bounded autonomy" mean?
The agent does the work it is trusted with and nothing more: least-privilege permissions, a person approving high-impact actions, testing before release and after every change, and a record of what it did and why.
When should we not use an agent?
When a simple rule, form or integration would do the job more reliably, when errors are costly and nobody can review the output in time, or when the data and systems the agent needs are not accessible or trustworthy yet. If an agent is the wrong tool, we say so.
How do you test AI agents?
Against realistic scenarios before release, and again after every model or prompt change, including tests for prompt injection, data leakage and tool misuse.
Do you support MCP?
Yes. We connect agents to your systems through well-defined, permissioned interfaces, including the Model Context Protocol (MCP) where appropriate.