Custom Agents aren’t “install and forget.” They’re not a shiny app you deploy and walk away from. They’re living systems that need care, context, and continuous improvement. We’ve already moved past the old waterfall playbook. With low-code and DevOps, we learned to ship faster, automate deployments, and iterate continuously. But here’s the catch: AI agents break even the DevOps mold. You will need an AI Agent Operating Model!
Why? Because agents don’t just run code. They reason, adapt, and depend on data and models that change outside your control. A pipeline alone won’t save you when a model update shifts behavior overnight or when your RAG index drifts. That’s why you need more than CI/CD. You need owners, guardrails, telemetry, and a cross-functional Agent Team that supervises, learns in production, and iterates quickly. The tech is moving weekly and your operating model must move with it.
In this post, I’ll share why the old playbook isn’t enough, it goes even further than DevOps. I will talk about a few principles that you need to govern and grow your agents safely.
The old project playbook breaks in the agentic world
For most of my career delivering CRM, apps, and automations, projects had a neat arc: discover, build, deploy, adopt. And after a couple of weeks; users will give feedback and together with a phase 2 a new iteration starts. It’s relatively slow and steady by design. But with AI agents, two assumptions collapse:
- Just as in most solutions the Agent is never “done.” Agents interact with messy business reality and external tools. Their behavior evolves with data, prompts, and model updates.
- “Go‑live” is the beginning in agent development, not the end. You only learn how an agent behaves when real users and real data hit it. So your operating model must assume continuous change.
First principle: Agent development is software development
There isn’t a magic prompt that will safely run a business process end‑to‑end. Agents require versioning, testing, CI/CD (where sensible), and observability across tool calls and reasoning steps. this is the core of emerging AgentOps and LLMOps practices.
Microsoft’s enterprise guidance says the same thing in plain terms: govern, secure, test, deploy, and monitor Copilot/agent experiences like any other production workload. Treat agent configuration and prompts as code. Build for iterative updates.
Second principle: Humans stay in the loop
All meaningful agents need supervision. Not because AI is bad, but because accountability, ethics, and edge cases live with us. NIST’s AI Risk Management Framework centers on human oversight, governance, and continuous risk management. In practice: define when people review, approve, or override. Don’t ship an autonomous loop without an escalation path.
Human in the loop interfaces are essential as agents take on higher‑stakes tasks. The maturity journey your agents, team and organization need to go through is from constant oversight to strategic oversight. But we will never remove the human entirely, that is not the goal.
Copilot Studio and the Power Platform provides several extensibility paths to help build this loop. Features like:
- Supervise agents in model-driven apps with agent feed – Power Apps
- Request information from humans in the loop – Microsoft Copilot Studio
- Multistage and AI approvals in agent flows – Microsoft Copilot Studio
Now those might not be sufficient for advances scenarios, but we are working on a low-code platform that allows you to extend and build what you need.
Third principle: Agents are data‑hungry and your data isn’t always ready
Unlike classic app logic, an agent’s performance depends heavily on data quality, freshness, and retrievability. If you’re using Knowledge sources, you will need stewardship on chunking, metadata, access control, and update cadences. If you do not involve this in your AI Agent Operating Model your agent will confidently fetch the wrong thing. Build RAG‑specific data governance and treat your knowledge sources like living products.
Data readiness (lifecycle, lineage, security) must evolve for gen‑AI or you’ll ship brittle assistants. Make data contracts and curation part of the plan, not an afterthought.
Fourth principle: The ground keeps shifting
Technology isn’t standing still and neither is your organization. The change hits on three fronts:
- Models change
Reasoning behavior, safety filters, and costs evolve with every update. Not only that, but what sometimes you will notice that what failed yesterday might work tomorrow. - Platforms change
Power Platform and Copilot Studio ship new capabilities at a relentless pace: dynamic chaining, orchestration features, MCP. These fundamentally change how you design and govern agents. Ignoring them means missing opportunities for resilience and efficiency. - Organizations change
Adding agents isn’t just a tech decision; it reshapes roles and responsibilities. Who owns the agent backlog? Who monitors compliance? Who retrains the team when new agents land? Without clear ownership and continuous enablement, your agents will outgrow your org structure.
Common pitfalls I keep seeing
- Treating prompts as the product. Prompts are part of a larger solution. Without data stewardship, human in the loop and monitoring your agent will drift.
- No owner team. If everyone owns the agent, no one does. Stand up the Agent Team and give it a backlog. Assign a business user mentorship to your agent as if it’s a new employee.
- Over‑optimizing for 100% correctness. Ship with supervision and a learning loop; improve with real usage rather than waiting for perfect.
If you take one thing away: be master of your own domain. Build your Agent Team now, instrument your agents like software, and establish the supervision and data practices that let you learn quickly in production, but do so safely. The tech will keep changing. With the right AI Agent Operating Model, you’ll change with it!