Every CTO is facing the same question right now: do you build on the Model Context Protocol (MCP) and plug LLMs into your existing tooling — or go full AI Agents and let autonomous systems own entire workflows?

They promise overlapping outcomes but demand completely different architectures, team skills, and risk tolerances.

In this episode we break down:

• What MCP actually gives you — standardized tool connections, stateless LLM-to-system bridges, and why Claude, Cursor, and Hermes all converged on it.
• When AI agents make sense — multi-step autonomy, cross-tool orchestration, and the work that can't wait for human-in-the-loop.
• The real tradeoffs — observability vs. autonomy, cost per outcome, and what happens when an agent goes off-rails at 3 AM.
• A decision framework — how to evaluate your stack, your team, and your threat model before picking a path.
• What comes next — why the answer might be neither, and how MCP servers and agents will likely coexist.

If you're architecting your AI strategy for the next 12 months, this is the conversation that clarifies what to bet on — and what to wait out.