AI Learning Ramp
Course 5 is a one-hour systems session on deciding what enters model context, what becomes durable state, what stays in the shared trace, and how production agents avoid drowning in their own history.
Assume your BigQuery-adjacent GenAI analyst needs to answer warehouse questions, draft SQL, inspect prior tool calls, and continue multi-step investigations across sessions. Your job is to design the context assembly layer: a governed pipeline that selects the right instructions, user intent, permissions, schema facts, retrieved evidence, conversation state, and compacted history without turning the prompt into an unbounded transcript dump.
One-hour objective: defend a context architecture for an enterprise analytics agent that balances relevance, state continuity, compression quality, cache efficiency, and long-context failure isolation.
List where an analytics agent loses quality: missing schema facts, stale permissions, bloated traces, conflicting tool outputs, hidden decisions, and context that cannot be recovered after compression.
Focus on the shift from prompt writing to context systems, especially just-in-time context, selection, compression, isolation, and agent memory boundaries.
Translate the write, select, compress, and isolate strategies into concrete controls for an analytics agent's context window.
Anchor on KV-cache-aware prefixes, append-only traces, stable tool definitions, file-system memory, objective recitation, and why some errors should remain visible.
Use the multi-agent critique to sharpen when subtask isolation breaks coherence, and why action traces often matter more than final summaries.
Sketch the context contract for SQL generation: permissions, semantic-layer definitions, schema evidence, query attempts, validator errors, and user intent.
State your context contract, shared-trace policy, compression boundary, external-memory plan, and one failure mode that your design deliberately prevents.
This version deliberately avoids product docs. The four required readings are practitioner articles from teams reasoning about context as an agent reliability problem.
A direct engineering treatment of context engineering as the next step beyond prompt engineering, with practical framing for just-in-time context, memory, selection, compression, and isolation.
Read for: the vocabulary and architecture tradeoffs behind reliable agent context assembly.
A strong organizing framework for agent context management that breaks the design space into write, select, compress, and isolate strategies, with examples from real agent products and papers.
Read for: a reusable taxonomy you can apply under interview pressure when the context window starts to fill up.
A production lessons-learned article on context loops in an agent used at scale: stable prefixes, append-only traces, tool masking, file-system memory, objective recitation, and error retention.
Read for: the operational mechanics that turn context engineering from a prompt idea into an agent runtime design.
A sharp agent-architecture essay from the Devin team arguing that context sharing and action traces are central to long-running agent reliability, and that naive sub-agent splits lose implicit decisions.
Read for: why shared context and trace continuity matter when designing agents that must stay coherent over many steps.
You are ready for the interview version of this topic when you can answer these without defaulting to "just use a bigger context window."
Prompt: design the context layer for a BigQuery analytics agent that answers questions, writes SQL, calls tools, and resumes investigations across sessions without leaking stale or unauthorized context.