AI agent development

Custom AI agent development for business workflows.

Build agents that can reason through a task, use the right tools, and take a controlled action—without pretending that autonomy removes the need for product and engineering judgment.

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10+ years engineeringProduction SaaS experienceDirect senior-engineer delivery

Make the agent accountable to the workflow.

An agent should have a job, a bounded set of tools, business rules, and a clear way to report what it did. The useful design question is not “How autonomous can this be?” It is “Which parts can safely be delegated, and where should a person remain in control?”

Where agents can help

Research and briefingGather information from approved sources and return structured findings for a person or downstream system.

Internal operationsRead context from internal tools, prepare actions, and route exceptions to the right owner.

CRM actionsEnrich, classify, summarize, or prepare updates while keeping write access explicit and reviewable.

Document workflowsExtract fields, compare documents, identify missing information, and ask for the next input.

Support assistanceFind relevant knowledge, draft a response, and hand off when the case needs a human decision.

ReportingCombine system data into a consistent report with source context and a defined output format.

A practical agent architecture

User / trigger→Agent→Model + rules→Tools→API / MCP / DB→Action

Validation, human approval, error handling, and an audit trail belong in the design—not as a later patch when the agent touches a sensitive workflow.

What I pay attention to

Tool permissions that match the task instead of broad access by default.
Structured outputs and validation before a write or irreversible action.
Retries, timeouts, fallbacks, and clear behavior when a tool is unavailable.
Observability that helps the team understand the input, decision, tool call, and result.

Related work

AI agent work often connects to MCP server development, API integrations, and workflow automation.

Questions buyers ask

AI agents without magical thinking.

Can you add an agent to an existing SaaS application?

Yes. The useful starting point is mapping the existing data, APIs, permissions, and workflow boundaries before deciding what the agent should be allowed to do.

How do you make an AI workflow reliable?

Use a defined task, constrained tools, structured outputs, validation, failure handling, observability, and human approval for actions that deserve it. Reliability comes from the surrounding system as much as the model.

What is the difference between an AI agent and normal automation?

Normal automation follows known rules. An agent can interpret context and choose among tools or steps, but it still needs boundaries and policy. Many good workflows use both.

Can an agent connect to our APIs?

Yes, when the API exposes the right operations and permissions. MCP can be useful as a tool interface, but it does not remove the need for sound APIs and access control.

Give the workflow a shape

Describe the decision the agent needs to support.

Tell me what the trigger is, which systems are involved, and where a human should remain in the loop.

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Start Your Project

Send the project brief.

Share the workflow, systems, and constraints. I’ll review the context and get back to you.

Prefer a conversation?

Book a 30-minute SaaS & AI Project Call.

Talk through the product, workflow, or engineering problem before deciding on the next step.

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