AI Agents & Automations
Custom AI agents that use tools, connect with business systems, and complete multi-step work with explicit limits and observable behavior.
What this covers
What does custom AI agent development include?
AI agent development turns a defined workflow into a system that can interpret a request, choose approved tools, perform controlled actions, and return a useful result. The work includes the surrounding API, state, permissions, logging, evaluation, fallbacks, and interface—not only the model prompt.
Technical Deliverables
- Workflow and tool-boundary definition
- Agent orchestration and state design
- Approved API, database, and platform integrations
- Structured outputs, validation, and fallback paths
- Backend endpoints, queues, and persistence where required
- Evaluation cases, logging, deployment, and handoff documentation
Where it fits
Problems this service can address
- A repeatable workflow still depends on copying information between several systems.
- A team needs AI to take approved actions, not only produce text.
- An existing prototype works in a demo but has no clear state, limits, failure handling, or operational visibility.
- Users need one interface across data, tools, and business rules that currently feel disconnected.
Plan before you build
Practical planning resources
Common Questions
What makes an AI agent different from a chatbot?
A chatbot primarily manages a conversation. An agent can also select approved tools, update state, call systems, and complete a multi-step workflow. Some products need both: a conversational interface in front of controlled agent behavior.
Can an agent work with our existing software?
Usually, if the required systems expose suitable APIs, databases, webhooks, files, or other controlled integration points. Discovery identifies what can be connected safely and where a custom adapter or human step is still needed.
How do you reduce unpredictable behavior?
The design narrows the task, constrains available tools, validates structured inputs and outputs, tests representative cases, logs important decisions, and adds confirmation or fallback paths where a model should not act alone.
Do we need a fully autonomous agent?
Often no. A smaller workflow with deterministic steps and AI only where interpretation is useful can be easier to operate. I recommend the least complicated architecture that can handle the real job.