Software Depo builds AI agents that do real work inside real businesses: they answer from your documents, act through your tools and APIs, and route sensitive steps through human approval. We are the SoftwareMile specialist practice for AI-agent development and intelligent automation.
What We Build
Knowledge and document agents that answer accurately from your policies, contracts, manuals, and tickets — with citations. Customer-service and employee-support agents that resolve routine requests and escalate cleanly. Workflow agents that move work between your systems: intake, triage, drafting, scheduling, reporting. Engineering, coding, and QA agents that work inside your repositories with scoped permissions. Multi-agent systems where specialized agents coordinate under an orchestrator with clear boundaries.
Built for Production, Not Demos
Every agent ships with scoped permissions, audit trails, evaluation suites run before rollout, monitoring in operation, and human-approval gates where actions have consequences. Private and hybrid deployments are supported when data cannot leave your environment.
Platforms We Implement
Custom agents on leading models; MCP servers and connectors so agents reach your systems safely; Microsoft Copilot Studio and Microsoft 365 Copilot; Claude and Claude Code setup including Skills, hooks, and MCP integrations; Codex setup including repository agents and Skills; retrieval-augmented generation and enterprise search.
How an Engagement Runs
Discovery maps the use case, data sources, tools, and approval requirements. We prototype against your real documents and systems, evaluate against agreed test sets, then deploy with monitoring and a support arrangement. You see behavior evidence before anything goes live.
Tell us about your agent use case — include the data sources and tools involved, your existing AI platform if any, and whether deployment must stay private. We will scope it from the first conversation.
Related services
- AI Agent Integration Services — Connecting agents to the systems you already run: AI agent integration.
- Agentic AI Consulting and Implementation — Not sure where agents fit yet? Agentic AI consulting.
What Is an AI Agent?
An AI agent is a system that uses a language model to decide and act, not merely to answer. Given a goal, it can choose which tools to invoke, read the results, and take further steps — looking a record up, updating a system, escalating to a person — rather than returning text and stopping.
The distinction from a chatbot is action and consequence. A chatbot that is wrong wastes someone’s time. An agent that is wrong creates a record somebody has to find and undo. That difference is what drives everything about how agents should be built.
What Makes an Agent Reliable Enough for Production?
- Bounded scope. Agents that do one job well are far more reliable than agents that do everything adequately.
- Scoped permissions. Acting as the requesting user rather than through a shared account with broad access.
- Human approval where it matters. On spending, customer contact, and deletion, until the logs justify removing the step.
- Evaluation before rollout. A test set of realistic tasks with known-good outcomes, run before deployment and again after every prompt or model change.
- Honest failure. An agent that says it cannot reach a system beats one that answers from stale context.
- Complete audit trail. What it did, why, on whose behalf.
Where Do Agents Actually Pay Off?
The strongest cases share a shape: a task done often, by several people, that requires pulling information from more than one system and applying rules that are written down somewhere. Support triage, quote preparation, document review, internal question answering over policies and procedures, and status reporting across tools all fit.
Weak cases also share a shape: work that is genuinely different every time, work where the judgement is the value, and work done rarely. Automating those costs more than it returns.
How Should a First Agent Project Be Scoped?
Pick one workflow with a named owner, a measurable cost in staff time, and a bounded blast radius. Ship it read-only first and watch what people actually ask for — it is reliably different from what was specified. Then add the single write action that saves the most effort, with approval on it.
This sequence produces a working system in weeks and real evidence about whether the wider programme is worth funding. Broad multi-department agent rollouts commissioned before anything has run in production are where budgets go to die.
Related: agentic AI consulting · agent integration · evaluations and guardrails.