Discuss agent architectures and share learnings with the community.
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.
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.
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.
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.
Google's Agent Development Kit is the same framework that powers Agentspace and its own production agent surfaces, open-sourced. Here is the complete map: agents, orchestration, tools, MCP, A2A, the dev UI, and a working enterprise build you can deploy with one call.
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.