Our Specialisation
Governed agentic AI for complex, high-stakes work
We design and deliver agentic systems with clear permissions, policy boundaries and cost limits. Human oversight, traceability and recovery planning are built into the workflow—so your teams can move forward with accountability and control.
How we build. What it takes to scale.
Defined agent identity and scope
Every agent is registered, versioned and scoped. No anonymous actors in production.
Least-privilege data and tool access
Agents access only approved tools and data sources. Permissions are defined at design time and enforced at runtime.
Action, risk and cost limits
Every action, including agent-to-agent hand-offs, is checked against central policy and metered for cost. Limits are enforced by rules, regardless of what the agent decides.
Human approval for sensitive decisions
Sensitive actions route to human reviewers through clear approval and escalation paths. Thresholds adapt to risk tier and context, so every consequential action has a person accountable.
Continuous monitoring and audit trails
Complete decision lineage: every agent action, reasoning step, tool call and cost is logged and queryable, supporting transparency, fairness reviews and regulatory audit.
Circuit breakers and rollback
Automatic containment when agents deviate from expected patterns, with the ability to recover safely.
Why It Matters
Why these controls matter for high-stakes work
High-stakes work leaves little room for recovery. A wrong recommendation can be corrected; an action taken against a customer, a payment or a filing usually cannot. These controls exist so AI can act with speed while the business still knows what is happening.
Defined agent identity and scope
When something goes wrong, you need to know which agent acted, on whose authority and what it was allowed to touch. Without that, an incident cannot be traced or contained.
Least-privilege data and tool access
A single mistaken decision should stay small. Agents that can reach only approved systems cannot turn one error into a wider problem.
Action, risk and cost limits
Limits set by rules, rather than by the model's own judgement, are what stop a reasonable decision becoming an expensive or irreversible one.
Human approval for sensitive decisions
Decisions with legal, financial or customer consequences need a person accountable for them. Approval paths make that accountability explicit.
Continuous monitoring and audit trails
Regulators, auditors and boards ask what happened and why. Complete decision lineage answers that directly, instead of being reconstructed after the fact.
Circuit breakers and rollback
Detection alone is not enough. When behaviour drifts, the system needs to stop and return to a known state without anyone improvising.
Start with one workflow that matters.
Tell us what you are planning. We'll review where oversight, access and recovery need to sit, and what responsible delivery would require.
Discuss your AI priorities