AI systems built around real work
Build AI agents that do real work
Build agents that find information, prepare work, use approved tools and ask a person to decide when needed.
Talk to usAn agent is more than a prompt
A production AI agent has to work with your data, permissions, systems and operating rules. It also needs boundaries: what it may do, when it must ask for approval, what evidence it should retain and how failures are handled.
Upcastr treats agent development as product and process engineering. We define the job, build the smallest useful version, test it against realistic cases and make its behaviour observable before it touches critical work.
Less manual preparation
Agents can gather, classify and draft routine work so your team starts with a useful first pass.
Connected workflows
Integrations let the agent work with approved business systems instead of operating as an isolated chatbot.
Controlled adoption
Permissions, human approvals, test cases and monitoring are designed into the solution from the start.
What we build
- Use-case and guardrail definition
- Knowledge retrieval and data connections
- Tool and API integrations
- Human approval and escalation paths
- Evaluation scenarios and acceptance criteria
- Deployment, monitoring and handover documentation
Common use cases
How we work
From problem to working change
Define the job
We specify the inputs, outputs, decisions, systems, users and failure conditions in plain language.
Build and evaluate
We develop an end-to-end version early, then test accuracy, usefulness, security and edge cases.
Release with control
We introduce the agent to a bounded group, measure performance and expand only when the evidence supports it.
Questions about AI agent development
What is an AI agent?
An AI agent is software that uses an AI model to complete a defined job, often by retrieving information, following a workflow or using approved tools. Unlike a general chatbot, a business agent should have a clear purpose, permissions and escalation rules.
Can an agent work with our existing software?
Often, yes. The exact approach depends on the APIs, access controls and data available in your systems. We assess those dependencies before committing to a scope.
How do you manage incorrect AI output?
We use task-specific evaluations, constrained data sources, validation rules, logging and human review where the consequence of an error warrants it. The right control depends on the job and its risk.
Talk to us about AI agent development
Tell us where work is slow or repetitive. We will help you decide whether AI, automation, training or a simpler fix is the right response.
Talk to usNo obligation. Start with the problem.