Home / AI agent development
AI agent development services
We design and build AI agents that work with your data, software and business processes. They can retrieve information, use internal tools, update systems and complete multi-step tasks within clearly defined limits. Our work covers the full system around the agent, including integrations, permissions, evaluation, monitoring, human approvals and operating cost control.
What we build into a production-ready AI agent
An AI agent is more than a language model. It combines the model with business data, connected tools, permissions and operating rules. We engineer each of these components so the agent can perform useful work reliably and within the boundaries set by your organisation.
Agent architecture and workflow design
We determine which tasks are suitable for an agent, which steps should remain deterministic and where human review is required. We also define whether the workflow needs one agent or several specialised agents and select the right model for each step.
Tool, API and MCP integration
We connect agents to internal APIs and business systems through controlled tools with typed inputs, limited credentials and complete call logs. Where appropriate, we build reusable MCP servers that can support multiple agents and applications.
Retrieval across company data
We enable agents to search documents, tickets, databases and knowledge systems while respecting the access permissions of the employee or customer making the request.
Evaluation and guardrails
We create evaluation suites based on real business tasks and run them after every significant change. Input and output controls help protect against prompt injection, sensitive-data exposure and actions outside the agent's approved responsibilities.
Monitoring and human approvals
Every run can be traced, reviewed and measured. We add alerts for failed or unusual behaviour and introduce approval steps wherever an incorrect action could create operational, financial or compliance risk.
Performance and cost optimisation
We measure token use, tool calls, response time and model cost for each workflow. Caching, model routing and smaller specialised models are introduced where they meet the required quality standard.
When an AI agent needs stronger engineering
Companies usually approach us when an agent is being planned, has reached a working prototype or needs to become reliable enough for production use.
Research is fragmented across multiple sources
Employees spend hours collecting information from documents, databases and external sources before a sales call, risk assessment or operational decision.
The work
- Sources and citations kept per finding
- The record filled in by the agent itself
- Findings spot-checked by the analyst
The agent selects the wrong tool or uses it incorrectly
The agent calls an unsuitable API, submits invalid arguments or produces inconsistent results, with limited visibility into how often the problem occurs.
The work
- Tool descriptions rewritten and tested
- Arguments validated before a call runs
- Wrong-tool rate tracked on the suite
Changes cannot be evaluated objectively
Prompt updates and model changes are reviewed through a small number of manual conversations, making it difficult to determine whether overall performance improved.
The work
- An evaluation suite built from real tasks
- Scores compared on every pull request
- Weak spots listed by kind of task
Existing systems were not designed for agent access
The agent needs to work with an API, database or file system originally designed for manual use and without the controls required for autonomous actions.
The work
- An MCP server or tool API per system
- Error replies the agent can act on
- Rate limits that protect the system
Agent workflows are too slow or expensive
A single request triggers too many model calls or repeated tool operations, increasing latency and making broader adoption commercially impractical.
The work
- Cost and latency traced step by step
- Repeated context cached between calls
- Independent tool calls run in parallel
A model change introduces unacceptable uncertainty
A newer model may offer better performance or lower cost, but the team cannot predict how it will affect existing workflows.
The work
- The suite rerun on the new model
- Changed answers read case by case
- The previous model kept ready to restore
From representative tasks to a production-ready agent
Our delivery process defines how the agent will be evaluated before development begins.
- 01
Workflow definition
We collect real examples from the people who currently perform the work. Together, we define the expected outcome, permitted actions, exceptions and decisions that must remain with a person.
Deliverables: representative tasks, success criteria and restricted actions - 02
Evaluation design
Each representative task becomes a repeatable test with a verifiable outcome. This establishes the performance standard the agent must meet before release.
Typical tools: LangSmith datasets and pytest - 03
Tools and access controls
We create a controlled tool for each permitted action, apply separate credentials and limits, and validate the integration in a test environment before production access is introduced.
Typical components: MCP servers, service accounts and role-based permissions - 04
Agent development
We develop the prompts, orchestration logic and model configuration, then iterate against the evaluation suite until the agent meets the agreed quality, speed and cost targets.
Typical tools: LangGraph and Claude Agent SDK - 05
Controlled release
The first release is limited to an internal group or selected customers. Every run is traced, and higher-risk actions remain subject to human approval.
Typical components: run tracing, approval queues and release controls - 06
Monitoring and continuous improvement
We review failed, slow and costly runs on an agreed schedule. Model, prompt and tool changes are released only after the evaluation suite confirms that performance remains within the required range.
Typical tools: Langfuse, monitoring dashboards and automated alerts
Where we can join your AI agent initiative
We can lead a new agent project, support an internal team, improve an existing system or assume responsibility for an agent developed by another provider.
Build a new AI agent
We take the project from workflow definition and evaluation design through integration, controlled release and production monitoring.
Strengthen your existing agent team
Our AI engineers join your repositories, development process and delivery cadence to support an agent your team has already started.
Hire AI engineersImprove an underperforming agent
We build an evaluation suite from the cases the agent handles poorly, identify the underlying causes and improve its prompts, tools, orchestration or model selection.
Take over and operate an existing agent
We review the current architecture, tools, prompts and run history before assuming ongoing responsibility for monitoring, model upgrades, integrations and improvements.
AI-assisted engineering with senior oversight
Coding agents help our engineers prepare tool wrappers, test cases and tracing code more efficiently. Senior engineers remain responsible for the decisions that determine whether an agent is safe and reliable, including what it can access, which actions require approval and when a workflow must stop.
AI-native product developmentBroader test coverage
Coding agents help generate variations of representative tasks, including missing information, conflicting records and hostile instructions. An engineer reviews each test and retains only scenarios that reflect realistic risks.
Security testing based on your environment
Senior engineers design prompt-injection and data-exposure tests around your actual workflows, such as malicious instructions hidden in emails, documents or uploaded files.
Human review for every connected tool
Before a tool becomes available to the agent, a senior engineer reviews the systems and data it can access, the actions it can perform and the safeguards applied to it. The same review standard applies regardless of how the code was produced.
Technology stack for AI agent development
We select models, frameworks and infrastructure according to the workflow, security requirements and systems the agent needs to access. Components are kept behind clear interfaces so they can be upgraded or replaced without rebuilding the entire solution.
Models
Different stages of a workflow may use different models. More capable models can handle planning and complex reasoning, while smaller models support focused classification, extraction and routing tasks.
- Claude
- GPT
- Gemini
- LLaMA
Agent frameworks and orchestration
We use established frameworks where they add value and clear, lightweight orchestration code where greater control and maintainability are required.
- OpenAI Agents SDK
- CrewAI
- LlamaIndex
Retrieval and company knowledge
We combine vector and keyword search to connect agents with company documents and structured data. Where practical, retrieval runs on databases and search infrastructure already used by your organisation.
- pgvector
- Elasticsearch
Evaluation, guardrails and observability
We test agent behaviour, inspect complete execution traces and apply controls to sensitive inputs, outputs and actions.
- Guardrails AI
- Presidio
Cloud infrastructure
Agent services, queues, secrets and monitoring run in your cloud environment and follow the same deployment standards as the rest of your software.
- Docker
- AWS
- Google Cloud
- Azure
Questions clients ask before launching an AI agent
How much does AI agent development cost?
The cost depends primarily on the scope of the workflow, the number of systems involved and the level of autonomy required. An agent that retrieves information requires fewer integrations and controls than one that updates records or initiates business actions. We provide a written estimate after discovery and calculate expected operating costs using real token consumption and tool usage from the evaluation suite.
What is the difference between an AI agent and an AI assistant?
An AI assistant primarily provides information, drafts content or supports a person in making a decision. An AI agent can also use connected tools and complete defined actions across multiple steps. The appropriate level of autonomy depends on the workflow, business risk and need for human approval.
Which frameworks and models do you build agents with?
We work with Claude, GPT, Gemini and open-source models, together with frameworks such as LangGraph, OpenAI Agents SDK, Claude Agent SDK, CrewAI and LlamaIndex. The final stack is selected based on required accuracy, response time, privacy, integration complexity and operating cost.
Can an agent use our internal systems safely?
Yes, provided access is designed around the principle of least privilege. Each tool receives narrowly scoped credentials, validated inputs and explicit rate limits. Higher-risk actions can require human approval, and every tool call can be logged for review and audit.
How do you test something that answers differently each time?
We evaluate the outcome of each task rather than relying only on exact wording. Tests measure whether the agent selected the correct tools, used valid arguments, followed operating rules, reached the expected end state and avoided prohibited actions. Representative tests are repeated after every material change.
Who owns the agent, its prompts and its tests?
You retain ownership of the code, prompts, tool definitions, evaluation datasets and documentation produced for the project. These assets can be stored in your repositories and cloud environment, with IP assignment covered by the contract.
What support does an agent need after launch?
Production agents require monitoring for failed runs, changes in model behaviour, rising costs and integration errors. They also need controlled updates when APIs, business processes or foundation models change. We can provide ongoing support or transfer the monitoring and release process to your internal team.
Discuss your AI agent initiative with our team
During the initial consultation, we will review the workflow, systems, users and approval requirements involved. Based on this information, our team will recommend which parts are suitable for agent automation, explain how the solution can be evaluated and prepare a preliminary estimate.
The proposed next steps will include an evaluation approach, the required system access and the input needed from your team.
- 1Initial workflow and systems review
- 2Evaluation plan and preliminary estimate
- 3Discovery kickoff within one week
We sign an NDA before discussing your data in detail. We reply within 24 hours.