AI Learning Ramp
Course 7 is a one-hour systems session on choosing when a BigQuery analytics assistant should be a deterministic workflow, a planner with tools, or a multi-agent handoff system with explicit control boundaries.
Assume the BigQuery copilot now handles ambiguous analytics work: clarify intent, inspect schema, draft SQL, run dry checks, repair failures, summarize answers, and escalate to a human when confidence is low. The interview question is not "can an agent do this?" It is where you deliberately use prompt chaining, routing, evaluator-optimizer loops, orchestrator-worker decomposition, and handoffs so autonomy increases only where the task benefits from it.
One-hour objective: explain the core agent patterns and defend where each belongs in a governed analytics assistant, including the failure modes of over-agentic designs.
Write one sentence for each mode: fixed workflow, model-driven workflow, single agent with tools, manager agent, decentralized handoff system.
Extract the operational difference between prompt chaining, routing, parallelization, orchestrator-workers, evaluator-optimizer loops, and agents.
Focus on when to split agents, how guardrails and tools constrain the loop, and why handoffs are a UX and authority boundary.
Compare agents-as-tools with handoffs for a BigQuery copilot: central manager control, specialist delegation, context transfer, and traceability.
Assign routing, evaluator-optimizer, orchestrator-worker, and handoff patterns to schema retrieval, SQL generation, validation, clarification, and support escalation.
Defend a minimal architecture, then name exactly what would make you add more agents: task diversity, independent expertise, context isolation, or user-facing ownership transfer.
Use three required sources: a pattern taxonomy, a practical agent-building guide, and concrete orchestration mechanics. Keep the optional refresher for deterministic workflow vocabulary only.
The highest-signal taxonomy for this session: workflows versus agents, prompt chaining, routing, parallelization, orchestrator-workers, evaluator-optimizer loops, and when simple composable patterns beat elaborate autonomy.
Read for: the vocabulary and tradeoff language interviewers expect when they ask about agentic system design.
A concise guide to agent components, tool use, guardrails, single-agent versus multi-agent designs, manager patterns, and decentralized handoffs.
Read for: how to turn the pattern taxonomy into an implementation plan with responsibilities, interfaces, and risk controls.
Concrete orchestration guidance for deciding whether specialist agents should be exposed as tools under a central agent or receive control through handoffs.
Read for: the architecture boundary between delegation, routing, user-facing transfer, and traceable control flow.
A short refresher on sequential, loop, and parallel workflow agents when you need crisp language for deterministic control flow.
Skim for: a reminder that not every useful agentic system needs autonomous planning at every step.
You are ready for the interview version of this topic when you can choose an agent pattern before naming a framework.
Prompt: design an agentic BigQuery analytics assistant that handles ambiguous business questions, generates SQL, validates results, and hands off to a human data owner when the answer is unsafe or underspecified.