AI agents go beyond answering questions: they plan steps, call tools and APIs, and update the systems your teams work in. That makes them valuable for repetitive multi-step work — and risky if they are given broad access without oversight. We build agents with the controls designed in from the first week.
Our agent development covers the whole system: mapping the workflow, designing the tools the agent can use, connecting them to your CRM, ERP, ticketing or data platforms through governed APIs, and building the evaluation set, action logs and approval steps that keep people in charge.
We start narrow — one workflow, a few tools, human approval on anything consequential — and widen autonomy only when measured results justify it. Our senior engineers have shipped software since 2010, including AI for aviation, life sciences and regulated industries where mistakes have consequences.
The AI work our agents build on
Our published case studies don't yet include a fully autonomous agent, so we won't claim one. These projects show the parts every production agent depends on: understanding complex requests, connecting to internal systems in real time and securing the data in between.
Generative AI chatbot for aviation customer service
A generative AI assistant that interprets complex aviation requests and handles quotes, service requests and support inquiries, connected to GA Telesis's internal systems for real-time processing behind a security layer.
Read the GA Telesis case study →
Generative AI platform for pharmaceutical R&D
Generative AI models embedded into Amgen's existing R&D infrastructure and validated against known outcomes with the research teams before results were relied on.
Read the Amgen case study →Custom AI agent development
Single-purpose agents built around one workflow, with tools, memory and clear stopping rules.
Multi-agent systems
Coordinated agents that split research, drafting, checking and execution, with a supervisor and audit trail.
Tool & API integration
Governed connections to your CRM, ERP, ticketing, email and data warehouse.
Human-in-the-loop design
Approval steps, review queues and undo paths for actions that matter.
Agent evaluation & safety
Test sets from real cases, prompt-injection defence, permission limits and monitoring.
Agent operations
Cost tracking, failure analysis, model updates and tool changes after launch.
Typical ai agent development engagements
Request triage & routing
Agents that read incoming requests, classify them, pull the right records and route work to the right team.
Quote & order preparation
Gathering specs, checking availability and drafting quotes for a person to approve and send.
Customer service agents
Agents that look up accounts, answer from your knowledge and complete simple requests, escalating when unsure.
Back-office operations
Reconciliation, data entry checks and record updates across systems that don't talk to each other.
Research & reporting
Agents that gather information from internal sources and draft structured reports for review.
Sales & CRM agents
Call summaries, follow-up drafts and CRM updates prepared for reps to confirm.
Controls before autonomy
Permissions, logs and approval steps are designed in week one, not bolted on after an incident.
Integration engineers, not prompt writers
Most agent work is connecting systems safely — that is what we've done since 2010.
Measured, not anecdotal
Every release is scored against real past cases before it touches live work.
Regulated-industry habits
Data minimization, redaction and audit trails from our healthcare and financial work.
Model-agnostic
Swap or mix providers as prices and quality change, without rebuilding the agent.
You own the system
Your repositories, cloud accounts, prompts, tool definitions and evaluation sets.
How to choose an AI agent development company
An AI agent is software that plans and carries out multi-step tasks using tools: looking up records, drafting replies, updating systems. That power is the risk. Use these questions with any AI agent development company, including us.
Start with a workflow, not an agent
Good agent projects begin with one workflow people already do by hand: triaging requests, preparing quotes, reconciling records, routing tickets. Map the steps, the systems touched and the decisions made, then decide which steps the agent does, which it suggests and which stay with a person.
- Which steps can the agent do alone, and which need approval?
- Which systems does it need to read from and write to?
- How will we measure whether it does the job well enough?
Agent, workflow automation or assistant?
Not every problem needs an autonomous agent. Fixed, predictable steps are often better as ordinary workflow automation. A copilot that drafts while a person decides is lower risk. Agentic AI earns its place when the task needs judgment across varied inputs and several tools. A good partner will tell you when a simpler design is safer.
Ask how permissions and actions are controlled
Agents should get the smallest set of permissions they need, through governed APIs rather than broad credentials. Every action should be logged with the reasoning and inputs behind it, and consequential steps — payments, record changes, customer messages — should wait for human approval or be easy to undo.
- Can we see every action the agent took and why?
- How do you defend against prompt injection from emails, documents or web pages?
- What happens when a connected system is down or returns bad data?
Insist on evaluation before autonomy
Agents should be tested on real past cases before they touch live work: did they choose the right tool, the right record and the right outcome? We build that evaluation set first, run every release against it and increase autonomy only as the measured success rate justifies it.
Plan for operations and ownership
After launch someone has to watch quality, cost and failure patterns, and update the agent when tools, policies or models change. Agree who does that, how models can be swapped without a rewrite, and make sure you own the code, prompts, tool definitions and evaluation sets.
- Healthcare & life sciences
- Financial services
- Aviation
- Enterprise operations
- Logistics
- SaaS
- 01Map the workflowSteps, systems, decisions and risks of the work the agent will take on.
- 02Build the evaluation setReal past cases with the expected tools, records and outcomes.
- 03Prototype with approvalA working agent that proposes every action for a person to approve.
- 04Harden & integrateGoverned tool access, logging, fallbacks and prompt-injection defences.
- 05Launch & monitorSuccess rate, cost and failure patterns tracked in production.
- 06Widen autonomyMore steps automated as measured results justify it.
What are AI agent development services?
Designing and building AI agents that plan and carry out multi-step tasks using tools and APIs — plus the integrations, permissions, evaluation, logging and human approval steps needed to run them safely in production.
What is the difference between an AI agent and a chatbot?
A chatbot answers questions. An AI agent takes actions: it can look up records, call APIs, update systems and move work forward across several steps. That is why agents need tighter permissions, action logs and review steps.
What is agentic AI?
Agentic AI describes systems that decide which steps to take toward a goal, use tools to take them and adjust based on the results, rather than following a fixed script. In business, it is most useful for varied, multi-step work that currently needs a person to coordinate several systems.
Can AI agents connect to our existing systems?
Yes. We connect agents to CRMs, ERPs, ticketing tools, email and data warehouses through governed APIs, giving each agent only the access it needs and logging every call.
How do you keep AI agents from making costly mistakes?
Least-privilege permissions, human approval for consequential actions, evaluation against real past cases before launch, monitoring in production and the ability to review or undo what the agent did.
How long does it take to build an AI agent?
It depends on the workflow, the number of systems involved and the approval rules. We scope one workflow first, give you a fixed scope and estimate after a short discovery, and release a supervised first version before widening autonomy.
Which frameworks and models do you use?
We use frameworks such as LangGraph and LangChain where they help, and models from OpenAI, Anthropic, Google and open-weight providers, chosen on measured quality, cost and data requirements.
Do we own the agent you build?
Yes. The code, prompts, tool definitions, evaluation sets and cloud accounts are yours, with documentation so your team can run and extend the agent.
AI agent guides and related services
- AI Development Services in 2026: The Buyer's Guide Most Vendors Don't Want You to ReadA buyer's guide to AI development services: scope, team, budget and red flags.
- Custom AI Development vs Off-the-Shelf AI Tools: What Should Your Business Choose?When a custom AI agent beats an off-the-shelf tool, and when it doesn't.
- AI Implementation & DevelopmentOur full AI practice, from strategy and models to MLOps.
- Generative AI DevelopmentLLM applications, chatbots and RAG systems over your own data.
- AI Consulting ServicesFind the workflows where an agent will actually pay off.
- Custom Software DevelopmentThe platforms and integrations your agents act inside.
AI Agent Development in production
Forecasting · Segmentation · MLOps
Facial recognition · Biometrics · Security
Put an AI agent to work on one real workflow.
Tell us the workflow, the systems it touches and where people lose time. We'll come back with a scoped, supervised first release.