AI agents
AI agent development
Software that does the task, not software that describes it.
- Sector
- Services
- What you get
- 7
Our approach
AI agent development
An agent is only useful if it can reach the systems where the work happens. We build agents that read your documents, query your databases, call your APIs and complete multi step tasks, with the permissions and the audit trail that make that safe to allow.
The hard part is not the model. It is deciding which actions an agent may take on its own, which need a human to confirm, and how to reconstruct afterwards exactly what it did and why. We design those boundaries first and build the agent inside them.
Agents run wherever the data has to stay: on your premises, in your cloud tenancy, or against a private model deployment. They are evaluated against your own cases, and monitored in production like any other system.
What you get
Artefacts you own and can hand to any engineer or auditor.
- Agent design: scope, tools, permissions and escalation rules
- Tool and API integration with your existing systems
- Retrieval over your own governed documents
- Human in the loop checkpoints for consequential actions
- Full action logging and audit trail
- Evaluation suite built from your real cases
- Monitoring, guardrails and rollback procedure
Outcomes you should expect
Outcomes you should expect
- Repetitive multi step work completed without a person relaying it
- Actions taken inside the systems you already run, not beside them
- A clear record of what every agent did and on whose authority
- Capacity that scales without adding headcount to a back office
Tools and platforms
Tools and platforms
- OpenAI, Anthropic and open weight models
- LangGraph and CrewAI
- Model Context Protocol
- Temporal for durable workflows
- pgvector and Qdrant
- OpenTelemetry for agent tracing
How we work
How we deliver
Best-practice process and development methodology, applied methodically.
- 01
Planning
We collect the relevant information from you and your stakeholders, including the constraints you have not written down.
- 02
Defining
We document the software needs, agree acceptance criteria, and write down what is out of scope.
- 03
Designing
System and interface documents are prepared against the specification: architecture, data model and stack.
- 04
Building
We implement in short cycles, with a working increment and a visible demonstration at the end of each.
- 05
Testing
We evaluate quality to find and fix defects: automated where possible, exploratory where judgement matters more.
- 06
Deployment
Release runs through an automated pipeline, checked for deployment issues, with a rehearsed rollback.
- 07
Maintenance
We keep the system current, secure and monitored, so you hear about problems from us first.
Questions
Frequently asked
What stops an agent doing something it should not?
Scoped permissions, allow lists on the tools it may call, and human confirmation for anything consequential. An agent should never hold more authority than the most junior person who could do the task.
Can we audit what it did?
Every action is logged with its inputs, the tool called, the result and the authority it ran under. Reconstructing a decision after the fact is part of the deliverable, not an extra.
Do agents need a hosted model?
No. Agents can run against a private deployment on your own hardware, which is often the only option when the documents they read are sensitive.
Related services
Related services
Let’s talk
Let’s engineer your next advantage.
Tell us what you are trying to achieve. You will speak to an engineer, not a call centre.
Monday to Friday, 08:00 to 18:00 East Africa Time (GMT+3). Urgent support by arrangement.