Clear, owned outputs.
- Agent scope and risk assessment
- Tool integrations
- Agent implementation with approval steps
- Audit logging and monitoring
- Evaluation results and rollout plan
AI agents go beyond answering questions: they plan steps and use tools — search, databases, APIs, documents — to complete tasks. That power needs careful limits. We design agents with narrow scopes, least-privilege access, approval steps for consequential actions and full audit trails.
The problems AI Agents solves.
Tool-using agents that complete multi-step tasks with permissions and approvals.
Multi-step tasks require switching between many systems.
Automation scripts break whenever inputs vary.
Giving AI access to systems feels risky.
It's hard to see what an AI system actually did.
What AI Agents covers.
Task design
Defining what the agent may do and when it must stop.
Tool integration
Connecting agents to APIs, databases and applications.
Permissions
Least-privilege credentials and scoped access.
Human approvals
Review steps before sending, paying, deleting or changing records.
Observability
Logs of every step, tool call and decision.
Evaluation
Testing success rates, failure modes and safety on realistic scenarios.
Guardrails an AI agent needs.
An agent doesn't just write text; it takes actions in your systems. That makes control and auditability the core of the design.
| What we check | Why it matters |
|---|---|
| Tool permissions | Each tool the agent can call is listed, with the narrowest permissions that do the job. |
| Human approval | Actions with financial, legal or customer impact wait for a person to approve them. |
| Step limits | Caps on the number of steps, retries and spend per task, so a loop can't run away. |
| Audit trail | Every decision, tool call and result logged for review. |
| Evaluation | Tested on realistic tasks, including ones designed to make it fail, before it touches production systems. |
Why it's worth it.
Tasks completed, not just answered
Agents carry work through several steps.
Flexibility
Agents handle variation better than rigid scripts.
Accountability
Approvals and logs keep people in control.
When AI Agents is the right move.
- Researching and preparing account briefs for sales.
- Triage and first actions on IT or support tickets.
- Gathering data from several systems to prepare a report.
Technologies & platforms
- Anthropic Claude
- OpenAI
- LangGraph
- Model Context Protocol
- Azure AI Foundry
- AWS Bedrock Agents
- Python
- FastAPI
How AI Agents work runs.
- 01
Scope
The task, users, content sources, risks and how success will be judged.
- 02
Prototype
A working prototype on real examples, reviewed by the people who will use it.
- 03
Evaluate
Test sets for accuracy, safety and edge cases, with human review.
- 04
Harden
Security, permissions, guardrails, logging and cost controls.
- 05
Launch and improve
Phased rollout, feedback loops and regular evaluation.
AI Agents questions.
Are AI agents safe to use with business systems?
They can be, with narrow scope, least-privilege access, approval for consequential actions, monitoring and testing. We don't give agents broad or unsupervised access.
How is an agent different from automation?
Traditional automation follows fixed rules. Agents decide which steps to take based on the situation, which makes them flexible but means they need stronger guardrails.
Where should we start?
With a well-understood task that has clear success criteria and low-risk actions, then expand once results are reliable.
Talk to us about ai agents.
Tell us which decision or process you want to improve and what data you have. We'll reply with whether it's a good fit and how we would test it.