AI Agent Development
Tool-using AI agents for bounded tasks, approved data sources, and multi-step workflows.
Use cases worth assessing
- 01
Research and knowledge assistants with explicit source access.
- 02
Internal agents that coordinate approved APIs and business tools.
- 03
Support copilots that propose actions for human approval.
What the engineering scope includes
- 01
A defined task, tool permissions, and escalation boundaries.
- 02
Application integration, state management, and audit-friendly event logs.
- 03
Representative task evaluations, failure cases, and human review points.
- 04
Deployment documentation and an agreed monitoring and iteration scope.
Build, evaluate, deploy, improve.
Each phase answers a different risk: business fit, data readiness, system design, AI behaviour, and operational reliability.
Discuss your AI projectDiscover
Define the users, workflow, data, constraints, and what success should mean.
Architect
Plan the application, model integrations, data access, permissions, and evaluation approach.
Prototype
Test a focused workflow with representative inputs before committing to a full build.
Build
Implement the product, integrations, interfaces, and observable AI workflow.
Evaluate
Check task quality, failure cases, access controls, latency, and running cost.
Deploy & improve
Release with monitoring, operational documentation, and a plan for iteration.
Questions about this service
AI Agent Development
What is the difference between an agent and a chatbot?
A chatbot typically responds in a conversation. An agent can select tools and execute steps toward a bounded goal. More autonomy creates additional requirements for permissions, state, evaluation, and human escalation.
Can an agent take actions in our tools?
It can use approved API integrations. We scope the actions it may take and add approval steps for sensitive or consequential operations. Tool access is not unrestricted.
How do you test agent reliability?
We agree representative tasks, expected behaviours, failure scenarios, and review criteria. Evaluations, logging, and escalation are part of the engineering scope; they do not eliminate every possible failure.
Can you use an existing model?
Yes. The starting point is normally a suitable existing model, evaluated against the workflow. Model choice and usage cost are assessed rather than assumed.
What determines agent development cost?
Workflow complexity, integrations, data access, permissions, evaluation coverage, and operational requirements determine the quote. Model and tool-provider charges also affect running cost.
Related AI services
- 01
AI Integration Services
Add LLM, retrieval, and assistant capabilities to the software and workflows your team already uses.
- 02
AI Workflow Automation
Connect business tools and automate repetitive operational work with visible decisions and human checkpoints.
- 03
AI MVP & SaaS Development
Validate a focused AI product and build the application, data flow, and operating model around it.
Discuss your AI project.
Share the workflow, product, or operational problem you want to improve.
Or email hello@arraybros.com
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