AI Agent Development

AI agent development from Software Depo is for organizations that want AI doing work, not just answering questions: reading the contract and drafting the response, triaging the ticket and filing it, checking the order status across three systems and telling the customer. We build agents that act – with the permissions, evaluations, and human approvals that make acting safe.

Agent vs. Chatbot

A chatbot answers from what it was given. An agent works: it retrieves from your documents, calls your APIs, updates your systems, and escalates when a step needs a human. That difference is architecture – tools, permissions, memory, and orchestration – and it is where most AI projects succeed or stall.

Agents We Build

  • Knowledge and document agents – accurate answers from policies, contracts, manuals, and tickets, with citations
  • Customer-service and employee-support agents – resolve the routine, escalate the rest with full context
  • Workflow agents – intake, triage, drafting, scheduling, and reporting across the systems you already run
  • Engineering, coding, and QA agents – working inside your repositories with scoped permissions
  • Multi-agent systems – specialized agents coordinating under an orchestrator with explicit boundaries

Production Discipline

Every agent ships with least-privilege tool access, an evaluation suite run before rollout, audit trails that reconstruct what the agent did and why, monitoring in operation, and human-approval gates on consequential actions. Connectivity runs through MCP servers we build as security boundaries. For independent security review, our sister practice BulletproofSoft assesses agent deployments – including ours.

Platforms

Custom agents on leading models; Claude and Claude Code with Skills, hooks, and MCP integrations; Codex repository agents and Skills; Microsoft Copilot Studio and Microsoft 365 Copilot; retrieval-augmented generation and enterprise search. Private and hybrid deployment when data cannot leave your environment.

Common Questions

How do we know it will not do something wrong? Scoped permissions limit what it can do; evaluations measure behavior before rollout; approval gates put humans on the consequential steps; audit trails show everything after. That stack, not hope, is the answer.

What does a first project look like? One use case, your real documents and systems, a working agent evaluated against agreed test sets – typically weeks, not quarters.

Our data cannot go to a cloud AI. Can you still help? Yes – private and hybrid deployments are a standard engagement shape, with a security onboarding step before any access.

Describe the work you want an agent to do – the documents and systems involved, and whether deployment must stay private. We will scope it honestly, including whether an agent is the right tool at all.