Hiring managers don't want another prompt course. They want evidence you can orchestrate rejection loops: eval harnesses, critic gates, and shipped agent workflows in public.
Every frontier jump improves generation faster than most teams improve verification. Enterprise risk compounds silently: more output, same taste perimeter.
Everyone name-drops hooks and skills. Almost nobody explains when to use which, and conflating them builds brittle agents that break on the second edge case.
The LangChain brand spans an abstraction library, a graph runtime, an eval/observability platform, and an expression language. Teams that blur them ship the wrong tool for the layer. Here's the clean mental model.
Tabular enterprise data needs schema-aware retrieval, SQL or semantic layers, and explicit join logic. Dumping rows into chunks and embedding them is why agents confidently cite the wrong quarter.
Glue-code automation wins when MCP tools are idempotent and the workflow owns retries, not when you chain hopeful API calls and pray the agent recovers.
ADK makes sense when you're already in Google Cloud and need governed agent deployment. The playbook isn't learning the SDK; it's eval gates, IAM boundaries, and critic loops.
Multi-agent bugs look like model failures but they're state-machine failures. The fix is replaying the critic's last rejection and finding the fork, not re-prompting the worker.
Self-hosted agents trade API convenience for data-plane control. For regulated industries that's not ideology; it's the only architecture legal will sign.
Stakeholders ask for 'productivity gains.' The honest metric is bad outputs caught before they ship: encoded judgment, not word count.
MCP standardization is real, but so is registry sprawl. The enterprise risk isn't picking the wrong model. It's building on a tool catalog you don't control.