Every vendor pitching an AI platform right now will tell you their agents are autonomous, their architecture is state of the art, and their roadmap solves problems you haven’t asked about yet. Somebody on the program has to separate what’s real from what’s demo-ready, and increasingly, that somebody is the Technical Program Manager.
This is a different skill than evaluating a traditional software vendor. GenAI and agentic systems introduce non-deterministic behavior, shifting model performance, and decision-making that can happen without a human in the loop. A TPM who treats an agentic architecture review like a standard build-vs-buy exercise will miss the risks that actually matter. Here’s a framework for doing it right.
1. Start With the Problem the Agent Is Solving, Not the Model Powering It
Vendor conversations tend to start with the model: which foundation model, which context window, which benchmark scores. None of that tells you whether the architecture fits your program. Start instead with the decision or task the agent is meant to own, and work backward. What does success look like for that task, what does failure cost, and how reversible is a bad outcome? A model comparison is a footnote to that analysis, not the headline.
2. Map the Decision Boundary: What Can the Agent Act on Without a Human?
Every agentic architecture has a decision boundary, whether or not the vendor has drawn it out for you. Push for a literal map: which actions the agent can take unsupervised, which require a human approval step, and which are off-limits entirely. If the vendor can’t produce this map cleanly, that’s the finding — not a footnote to raise later, but a blocker to resolve before the program moves forward. A TPM’s job here is the same as it is in any architecture review: force the ambiguous parts of the system into writing.
3. Interrogate the Data and Tooling Dependencies
Agentic systems are only as reliable as the data they read and the tools they’re allowed to call. Ask what internal systems the agent connects to, what permissions those connections carry, and what happens if a downstream API changes or goes down mid-task. This is standard technical due diligence, but it matters more here because an agent can chain several of these dependencies together in a single unsupervised run. One brittle integration doesn’t just fail — it can cascade.
4. Stress-Test for Failure Modes, Not Just Happy Paths
Vendor demos are built around the happy path by design. Your job is to find the other paths. Ask what the agent does when it receives ambiguous input, when a tool call returns an error, or when it’s asked to do something slightly outside its intended scope. Push specifically on hallucinated actions — not just hallucinated text, but the agent confidently taking a wrong action and reporting it as completed. If the vendor doesn’t have a good answer for what happens next, that gap becomes a program risk you’ll own.
5. Build in Observability Before You Build in Autonomy
A recurring pattern in early agentic rollouts is teams granting more autonomy than their monitoring can support. Before expanding what an agent is allowed to do on its own, confirm you can answer three questions after the fact for any given run: what the agent decided, why it decided that, and what it changed. If those questions require reconstructing logs from three different systems, the architecture isn’t ready for expanded autonomy, regardless of how well it performed in testing.
6. Score the Architecture Against Business Outcomes, Not Technical Novelty
It’s easy for an architecture review to turn into an evaluation of how sophisticated the AI is. Redirect it. Score the architecture against the same criteria you’d use for any program investment: does it reduce cycle time, reduce error rate, or reduce cost, and by how much, measured against a defined baseline. A technically elegant agentic system that doesn’t move a business metric isn’t a program win — it’s a science project with a production budget.
The Bottom Line
Evaluating GenAI and agentic architectures isn’t a specialized skill separate from technical program management — it’s the same rigor TPMs already apply to vendor evaluation, risk assessment, and system design, pointed at a newer class of technology. The TPMs who get this right won’t be the ones who know the most about transformer architectures. They’ll be the ones who ask the questions that keep a program out of trouble six months after launch.
Want a structured way to build this skill? TPM Institute’s Technical Program Management for GenAI and Agentic Systems course walks through architecture evaluation, agent governance, and program scoping in a live, instructor-led format. Visit tpminstitute.org to learn more.
