AI Agent Development Services
We build autonomous agents that plan, use tools and complete real workflows. Architecture, evaluation, red-teaming and operation, engineered so they are safe to run in production.
What AI agents do for business
An AI agent is a system that plans, calls tools and takes multi-step actions toward a goal. The value is real, and so is the risk: an agent that acts on your systems needs guardrails, not just a good prompt.
We design the agent architecture, build the tool integrations, put an evaluation harness and red-team pass in place, and operate the result so it stays reliable as your tools and data change.
- Multi-step, tool-using agents scoped to a real workflow.
- Evaluation and red-teaming before anything touches production.
- Human approval gates and audit trails where they matter.
Custom AI agent development
A custom AI agent is designed around one job in your company. We define what the agent should achieve, which tools it can use, what it must never do and when it hands over to a person. Then we design the parts: the planning loop, the tool connections to your systems, the memory it needs for context, and an evaluation set built from real cases.
Evaluation decides whether an agent is ready. We test it against the cases your team handles every day, including the awkward ones, and measure task success, errors and cost per task before it goes live.
Agent use cases we build
- Sales: qualifying inbound leads, researching accounts and preparing the CRM record.
- Customer service: resolving standard requests such as order status or address changes end to end.
- Finance: matching invoices to orders and receipts and flagging exceptions.
- Procurement: comparing supplier offers and preparing a decision summary.
- IT operations: triaging tickets and resolving routine requests such as access changes.
- Research and reporting: collecting information from internal and external sources into a structured brief.
Guardrails, identity and audit
An agent with access to company systems needs the same controls as a new employee. Every agent we build gets its own identity, permissions limited to what its task requires, and a complete log of every action and tool call. Sensitive steps such as payments, contract changes or customer refunds need human approval.
We also protect agents against prompt injection, where instructions hidden in an email or document try to take control, and set limits on cost and the number of steps per task.
Frameworks we use
We pick the framework that fits the task rather than one for everything. LangGraph works well for agents with explicit state and branching. The OpenAI Agents SDK and comparable SDKs from other model providers suit simpler tool-using agents. For high-volume or strictly regulated processes we build our own orchestration on top of your workflow engine, which gives full control over state, retries and audit.
The agent is not tied to one model. We can switch between providers such as OpenAI, Anthropic or Mistral, or open models hosted in the EU, as quality and cost change.
How we build agents
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Architecture and tools
We design the planning loop, the tools the agent can call and the boundaries it must respect, matched to your systems.
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Evaluation and red-team
An evaluation harness against real tasks plus adversarial testing, so failure modes are found before users are.
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Guardrails and operation
Approval gates, audit trails and monitoring so an agent can act with the right level of autonomy and oversight.
Frequently asked questions
What is an AI agent?
Software that uses a language model to plan steps, call tools such as APIs or databases and complete a task, rather than only answering a question.
Are AI agents safe to use with company systems?
They are when each agent has limited permissions, its own identity, logged actions and human approval for sensitive steps. We build all four in.
Can small companies use AI agents?
Yes. Start with one narrow task, such as qualifying inbound leads, and expand once it works reliably.
How long does it take to build an AI agent?
A first working agent for one process usually takes four to eight weeks, including testing on real data.
Put an agent on the repetitive work
Tell us the workflow you want an agent to own. We will scope an architecture that is reliable and safe to operate.