AI Development
AI agents and multi-agent systems that carry out real work, engineered for production and governed from the first day.
Overview
Artificial intelligence delivers value when it completes a task, not when it produces a demonstration. Our AI development practice builds autonomous agents that plan, use tools, exchange work with one another and return a result your organization can rely on. Each engagement begins with a single, clearly bounded workflow and an agreed definition of success, so that progress can be measured rather than assumed.
We treat an agent as a piece of production software. It is version-controlled, tested against real examples, observed in operation and constrained by explicit rules about what it may and may not do. Where a decision carries financial, legal or reputational weight, a person remains in the approval path. The outcome is automation that your teams trust and your auditors can follow.
What we deliver
Task-specific agents
Agents designed for one defined responsibility, such as classifying incoming requests, preparing a first-draft proposal or reconciling records between two systems.
Multi-agent systems
Coordinated teams of agents in which planning, research, execution and review are separated, so that each step can be inspected and improved independently.
Workflow orchestration
Stateful workflows built with LangGraph and CrewAI, including branching, retries, time-outs and human approval at the points that require it.
Tool and system integration
Secure connections between agents and the systems they act upon: CRM, ERP, ticketing, email, document stores and internal APIs.
Evaluation and guard rails
Test suites built from your own cases, automated scoring of every release, and policy checks that prevent an agent from acting outside its remit.
Operations and observability
Logging of every step and decision, cost and latency tracking, alerting, and a review loop that turns production findings into improvements.
Our approach
- 1Select the workflow
We work with the people who perform the task today, document each decision and hand-off, and confirm that the process is suitable for automation.
- 2Design the agent system
Roles, tools, data access and approval points are defined, together with the measures by which the system will be judged.
- 3Build and evaluate
A working system is delivered into a test environment and scored against real examples until it meets the agreed threshold.
- 4Deploy and govern
The system is released with monitoring, access controls and a documented procedure for review, escalation and change.
What you receive
- A production-ready agent system, with source code and documentation
- An evaluation suite built from your own cases
- Operational dashboards for quality, cost and latency
- A governance guide covering permissions, approvals and escalation
- Knowledge transfer to your engineering and operations teams
When this service fits
This service is well suited to processes that are repetitive, rule-based and already documented, where volume is high and a person continues to approve the result. It is particularly effective in customer support, sales operations, human resources, reporting and back-office reconciliation.
Questions and answers
How do you keep an agent from taking an action it should not?
Each agent is granted only the tools and data its role requires. Actions with material consequences pass through policy checks and, where appropriate, human approval. Every step is logged and can be reviewed.
Which model providers do you work with?
We are provider-neutral. Systems are designed so that the underlying model can be changed as quality, price and data-residency requirements evolve.
How is success measured?
Before development starts we agree on measurable criteria, such as accuracy against a reference set, handling time and the share of cases completed without intervention.