• Workflow too complex for a simple AI feature -- needs to plan, use tools, and adapt at each step?

  • AI prototype that works in demos but fails in production when real-world edge cases appear?

AI Agent Development

An AI agent does more than generate text -- it plans, uses tools, executes actions, and adapts based on what it gets back. That means querying your database, calling your APIs, reading and writing documents, and making decisions at each step based on intermediate results rather than a fixed script.
We build AI agents for production: from focused single-purpose agents that automate one specific workflow to orchestrated multi-agent systems that handle complex tasks requiring different capabilities at each step. LangGraph for stateful workflow management, human-in-the-loop checkpoints where the stakes require it, and monitoring infrastructure so you know what your agents are doing.

  • Single-purpose and multi-agent systems built for your specific workflow

  • Tool-using agents with database, API, document, and web search access

  • LangGraph orchestration for stateful multi-step workflows with checkpoints

  • Production monitoring, failure handling, and cost management included

An AI agent is an AI system that executes multi-step tasks autonomously by reasoning through a problem, selecting and using tools (APIs, databases, search), processing results, and deciding next steps in a loop until the task is complete. Agents are the right choice when a task requires decision-making at intermediate steps, not just a single prompt-response interaction. They differ from simpler AI features in that they have access to tools, maintain state across steps, and can handle branching logic based on what they encounter.

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Calorgas
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Bank of America
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Valero
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East Ventures

Most AI projects start with a single prompt and a single response. That covers a lot of ground -- classification, extraction, summarisation, generation. But it hits a ceiling when the task requires decision-making mid-way through, when the AI needs to look something up before it can proceed, or when the output of step three depends on what the AI found in step two.

That is where AI agents come in. An agent runs a reasoning loop: plan the next action, execute it using a tool, process the result, decide what to do next. It handles the variable, branching, multi-step work that a single prompt cannot. The complexity of building agents reliably is in the orchestration, the failure handling, and the evaluation -- not the prompting.

What we build

Single-purpose AI agents

Focused agents designed to automate one specific workflow reliably: a research agent that gathers and synthesises information from multiple sources, a data processing agent that extracts and transforms records, or an outbound agent that drafts and sends follow-up communications. Defined inputs, defined tools, defined outputs, and an evaluation framework that confirms the agent is performing correctly before and after deployment.

Multi-agent orchestration systems

Orchestrated systems where multiple specialised agents work in sequence or in parallel -- an orchestrator that decomposes the task, worker agents with specific responsibilities, and a synthesis agent that produces the final output. Used when a single agent can't reliably handle the full range of reasoning required, when parallel processing improves throughput, or when a validation agent is needed to check primary agent outputs.

Tool-using agents with API access

Agents equipped with the tools they need to complete their specific tasks: database query tools, REST API calls, web search, file read and write, calendar and email access, and CRM or ERP integrations. Tool definitions, input validation, and output parsing built to handle the real-world variability in what those tools return. Agents that use your existing systems rather than requiring separate data pipelines.

Document processing agents

Agents that process documents end-to-end: extract structured data from unstructured contracts, invoices, or reports; validate extracted fields against business rules; route documents based on content; and trigger downstream actions based on what they find. Handles the variability in document formats that breaks simpler extraction pipelines. Includes confidence signals and human review queues for low-confidence extractions.

Customer-facing AI agents

AI agents embedded in customer-facing products: support agents that resolve queries by accessing order history, product databases, and knowledge bases; onboarding agents that guide users through setup workflows; and qualification agents that gather information and route to the right team. Conversational interfaces over web, mobile, or voice with context preservation across the session and escalation to human agents when needed.

Agent monitoring and evaluation

Production monitoring for deployed agents: trace logging of every reasoning step, tool call, and decision; cost-per-run tracking; latency monitoring; and quality evaluation sampling. Evaluation test suites covering the range of inputs your agent encounters in production. Alerting on failure rates, cost spikes, and quality degradation. The infrastructure that tells you what your agents are doing and whether they are doing it correctly.

Workflow needs more than a prompt and a response?

Tell us the task you need automated, the tools it requires, and the decision points along the way. We'll design the agent architecture and give you a fixed cost.

Talk about your AI agent project

Frequently asked questions

An AI agent reasons through a task, selects tools to use, executes tool calls, processes the results, and decides what to do next -- repeating this loop until the task is complete. A simpler AI feature takes an input and returns an output in a single step. You need an agent when your use case requires: decision-making at intermediate steps based on what the AI discovers, access to tools like databases or APIs to complete the task, handling of variable task paths that can't be pre-scripted, or multiple sequential actions before producing a final result. If your use case is a single input-to-output transformation -- summarise this, classify this, extract from this -- a simpler AI feature is usually sufficient.

Agent failures in production fall into two categories: tool failures (an API call returns an error, a database query returns no results) and reasoning failures (the agent takes a wrong path or produces an output in an unexpected format). We handle tool failures with retry logic, fallback paths, and escalation to human review when the agent can't recover. We handle reasoning failures with output validation at each step, structured output schemas that prevent format errors, and evaluation test suites that catch regressions. Human-in-the-loop checkpoints are added for high-stakes decisions -- the agent prepares a recommendation and a human approves before action is taken.

We use LangGraph as our primary orchestration framework for stateful multi-step agents -- it models agent workflows as directed graphs with defined state, transitions, and human-in-the-loop interruption points. For simpler agents we work directly with the OpenAI Assistants API or build lightweight orchestration layers without a framework dependency. We have also built systems with CrewAI for role-based multi-agent patterns. Framework selection is based on your workflow complexity and operational requirements -- LangGraph for complex stateful workflows, simpler approaches for focused single-step agents.

A single-purpose AI agent -- one workflow, defined tool set, production deployment with monitoring -- typically runs $30,000--$80,000. Multi-agent orchestration systems with multiple specialised agents, complex tool integrations, and full evaluation infrastructure run $80,000--$250,000. Cost depends on workflow complexity, number of tools and integrations, agent count, and the human-in-the-loop requirements. We scope every project before pricing it and provide a fixed-cost proposal.