Custom AI Agent Development Built to Act with Control

An AI agent is software that can interpret a goal, use relevant data and tools, decide which steps to take, observe the results and continue until the task is complete or human input is required. Unlike a simple chatbot, an agent can work across multiple steps and take controlled actions inside connected business systems.

Our service can cover opportunity discovery, workflow analysis, agent architecture, model and platform selection, RAG and memory, tool integration, user experience, human approval paths, evaluation, security, deployment, monitoring and ongoing optimisation. The final scope is based on the process, systems, data, risk and business outcome involved.

A chatbot primarily answers questions or follows a defined conversation. An AI agent can also plan, call tools, update systems, coordinate tasks and adapt its next step based on results. Some customer-facing agents include a conversational interface, but their value comes from controlled action and workflow completion rather than conversation alone.

Yes. We can connect agents with CRM, ERP, SharePoint, Microsoft 365, Salesforce, service-management platforms, databases, document repositories and bespoke applications through APIs, events and secure integration layers. Identity, permissions, data contracts, audit requirements and failure handling are considered as part of the architecture.

Yes. We use the simplest architecture that can reliably achieve the required outcome. A single agent or deterministic workflow is often preferable for a focused process. Multi-agent orchestration can be appropriate where specialist roles, parallel work or structured hand-offs create measurable value. We validate the additional complexity before recommending it.

Controls are designed around the actions an agent can take and the consequence of error. They can include least-privilege access, scoped tools, approval checkpoints, restricted actions, data minimisation, environment separation, secrets management, audit logs, stopping conditions, output filtering and human escalation. Legal and operational responsibility remains with the organisation using the agent.

We create representative task sets and failure scenarios, then measure task completion, answer quality, tool selection, action accuracy, policy compliance, latency and cost. Testing can include automated evaluations, regression suites, adversarial scenarios, human review and production feedback so changes to prompts, models, tools or data are assessed before wider release.

Yes. We can review the prototype, architecture, prompts, data, tools, integrations and evaluation evidence, then identify what is required for production. Typical work includes strengthening permissions, error recovery, observability, user experience, test coverage, cost controls, deployment, support ownership and change governance.

Timelines depend on workflow complexity, system access, data readiness, required integrations, autonomy level and assurance needs. A focused discovery and pilot may take a few weeks, while a production agent connected to several enterprise systems is usually delivered in phases over several months. We provide a milestone-based plan after the initial assessment.

Cost depends on discovery effort, workflow complexity, model and platform choices, integrations, data preparation, user interfaces, security, evaluation, deployment scale and ongoing support. We first define the outcome, autonomy boundaries and delivery assumptions, then provide a transparent scope and commercial proposal.