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Samer Hany

Production AI Agents

Build AI agents that interpret real work, use your tools, and operate within boundaries you can inspect and control.

An AI agent becomes useful when it can do more than produce text. It needs a defined job, access to the right data, permission to use specific tools, and a clear point where it must stop and ask a person. I build agents for production workflows: classifying inbound work, extracting structured data, coordinating steps, updating systems, and proposing consequential actions for approval.

Describe your process

You may recognize this if

  • A person spends hours reading, classifying, and routing emails, documents, or requests.
  • Your prototype can answer a prompt but cannot safely use real systems or permissions.
  • The work needs interpretation and context, so a fixed rules engine cannot handle the normal variation.
  • You need the agent to update tools, call APIs, or trigger workflows rather than only suggest what someone should do.
  • Some actions can run automatically, while financial, external, or irreversible actions must wait for approval.
  • You cannot inspect why the current system made a decision or what happened between input and output.

How it is done

  1. Give the agent one bounded job

    The first version gets a clear responsibility, inputs, outputs, permissions, and stopping conditions. A narrow agent that can be trusted is more useful than a general one nobody can safely deploy.

  2. Keep deterministic work deterministic

    Validation, calculations, permissions, and fixed business rules stay in conventional code. The model is used where language, classification, or context genuinely requires it.

  3. Connect tools with least-privilege access

    The agent can read and act only through explicit tools and within the permissions of the person or process invoking it. It never receives broader access for convenience.

  4. Put approval at the point of consequence

    Low-risk routine work can continue automatically. Anything financial, external, or difficult to reverse is proposed with context and waits for a person to confirm it.

  5. Evaluate and trace the whole run

    Inputs, tool calls, decisions, outputs, latency, and failures are recorded so behaviour can be tested before release and investigated after it runs.

Proof

Business-management and financial software · Belgium

90% of supported administrative workflows can be completed in conversation, with confirmation before anything consequential.

Related solutions

This is part of something broader

This is one practical form of AI Agents & AI-Powered Solutions. A wider project may combine it with applications, integrations, reporting, or AI depending on where the process is breaking today.

AI Agents & AI-Powered Solutions