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.
Model Context Protocol servers look simple in a tutorial. In enterprise they need auth boundaries, schema versioning, blast-radius limits, and observability. Otherwise they become your next outage.
Every frontier jump improves generation faster than most teams improve verification. Enterprise risk compounds silently: more output, same taste perimeter.
Short-term scratchpads, long-term knowledge stores, and session boundaries each solve different problems. Treating chat history as memory is why your agent forgets what mattered.
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.
Model, memory, tools, planner, orchestrator, eval: six components with explicit contracts between them. Skip the contracts and you get a demo that collapses in week two.
Letting agents riff produces great demos and terrible audits. Explicit planner–worker–critic loops encode rejection at the orchestration layer. The same move as scaling-your-no, but structural.
If your planner, worker, and critic all hit a frontier model, your margin dies. Cost architecture; model routing by task criticality: is as important as prompt architecture.
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.
A three-person team ships production software no human writes or reviews. Down the hall, experienced developers get measurably slower with AI, and never notice. The distance between them is the most important gap in software, and no tool can close it.
LangGraph makes state explicit; CrewAI abstracts roles. Neither is 'better'. The choice is whether your team needs inspectable graphs or faster role-based prototyping.
Generation is solved. The bottleneck is judgment, and the specific, learnable, scalable form of judgment is saying no to confident AI output, and knowing exactly why. Most teams let every one of those noes fall on the floor.
Agent observability needs span-level tool attribution, critic decisions, and replayable traces, not aggregate token dashboards that hide the fork where everything went wrong.
I've replaced three 'RAG is solved' pipelines this year. The pattern is always the same: layout-aware parsing, hybrid retrieval, and a reranker, not a bigger embedding model.
I/O, Cloud Next, Build, re:Invent, and Interrupt across one season. The throughline isn't a winning framework; it's that the whole industry quietly admitted reliability and governance are the unsolved part.
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.