Custom AI Agent Development
AI agents that do the task, not just describe how to do it.
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An AI agent is a system that can plan and carry out a multi-step task using tools — looking things up, calling an API, updating a record — rather than just generating text in response to a single prompt. DuCodes builds custom AI agents scoped around one real workflow at a time, with explicit limits on what actions the agent is allowed to take on its own.
The difference between a chatbot and an agent is action: a chatbot answers a question, an agent completes a task — checking inventory, drafting and sending a follow-up, updating a CRM record, triaging a support ticket. That requires giving a model access to tools (APIs, databases, internal systems) and deciding, deliberately, what it's allowed to do unsupervised versus what needs a human to approve first.
We build agents around one concrete workflow at a time rather than a general-purpose "do anything" assistant — narrower scope makes the failure modes predictable, and predictable failure modes are what make an agent safe to actually deploy.
Why work with DuCodes on this
Scoped tool access
An agent only gets the tools and permissions it actually needs for its task — we don't wire up broad system access for convenience.
Human-in-the-loop where it matters
Actions with real consequences (sending an email, charging a card, deleting a record) get an approval step by default unless you explicitly want full autonomy.
Built to fail safely
When the agent can't complete a step confidently, it stops and reports rather than guessing its way through the rest of the task.
One workflow, done well
We'd rather ship an agent that reliably handles one real process than a general assistant that handles many processes unpredictably.
How we work
Map the workflow
The exact steps a human currently takes to complete this task, and which of those steps are safe to automate.
Define tools and permissions
What APIs/systems the agent needs access to, and exactly what it's allowed to do with that access.
Build with checkpoints
Approval steps for consequential actions, and clear logging of every action the agent takes.
Test against edge cases
Deliberately running the agent through the unusual inputs and failure paths, not just the happy path.
Launch with monitoring
Ongoing visibility into what the agent is doing in production, so unexpected behavior gets caught quickly.
Technology we use
Frequently asked questions
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