orchestration

Complex tasks rarely fit one prompt, and free-text LLM output rarely drops into code cleanly. What matters is decomposing into chained steps and parsing each output. Here is the answer that ties prompting to real pipelines.

Agents get reached for too often. A strong answer pushes past the hype: most tasks are better served by a single structured call, and agents pay off only under particular conditions. Here is when one is worth it and how to stop it from compounding errors.

Multi-agent is the pattern most often reached for without cause in AI today. The signal is holding back unless the task genuinely calls for specialization or parallelism, knowing the supervisor and handoff patterns, and grasping why coordination magnifies failure.

Plan-and-Execute and ReAct are the two leading agent control patterns. The signal is knowing the upfront-plan versus step-by-step tradeoff, why one costs less, and why most production agents wind up blending them.

Sub-agents are how you scale a complex agent task without one huge, noisy context. The signal is context isolation and clean delegation: a focused subtask in, a compact result out. Here is the answer.

Multi-agent systems succeed or fail on how agents share information and hand off work. The signal is naming the three communication patterns and the coordination failures that sink the naive version. Here is the answer.

Pushing every query through one pipeline wastes latency, money, and precision. A router decides what each query actually needs. The signal is naming the four decisions a router makes and how you stop a misroute from sinking the answer.

An agent that retries the same failed action 40 times means a runaway bill and a stuck user. Termination is something you build into the harness, not something the model reliably decides. Here is the control layer.

Frameworks promise speed and hand you abstraction you'll eventually fight. Rolling your own is more code but full control. Here is the honest tradeoff, and why many production teams end up thinner than they started.

Multi-agent systems coordinate through handoffs and shared state. The signal is knowing the orchestration topologies, what a clean handoff protocol carries, and why naive multi-agent often loses to a single good agent.

Interviewers ask about these by name and listen for whether you have read the source or only the README. The signal is explaining the runnable interface, why an agent needs a cyclic graph rather than a chain, and what the layers hide.

Agents that loop for minutes, invoke tools, and spawn sub-tasks demand durable execution rather than one request handler. See how to checkpoint state, recover after failures, cap runaway loops, and trace every move a non-deterministic agent makes.

ML workflows are multi-step DAGs (ingest, feature, train, eval, deploy), and orchestrators run them reliably. What matters is the DAG/scheduling model plus what's genuinely ML-specific: data deps, versioned artifacts, drift triggers, eval gates.

These four get confused constantly, but they sit at different layers: data processing, distributed training, and general orchestration. The signal is fitting the tool to the workload instead of reaching for the one you know. Here is the answer.