Defining agentic systems, task decomposition and planning, multi-agent orchestration, and reliability and human oversight — the foundation every other domain builds on.
An agentic system is defined by three things working together: autonomy over multiple steps, the ability to call tools that act on the world, and an action loop that lets it observe the result of one action before deciding the next. Take away any one of those and you have something else — a single-shot completion, a fixed pipeline, or a chatbot.
The exam draws a hard line between three patterns: a conversational system responds turn by turn with no independent action loop; a workflow executes a fixed, predetermined sequence of steps regardless of what it finds along the way; an agent decides its own next step based on what the previous step returned.
Reach for agentic architecture only when the task's shape can't be known in advance — variable-length research, open-ended debugging, multi-system orchestration. It costs more in latency, tokens, and predictability than a workflow, so a fixed pipeline is usually the better engineering choice whenever the steps are actually known ahead of time.
Full breakdown & examples → 1.1 Foundations
Before an agent acts, it has to break an ambiguous goal into concrete steps. Good decomposition separates steps that must happen in order from steps that are genuinely independent, so independent work can run in parallel instead of serially burning time and tokens.
Plans should not be treated as fixed once written. Dynamic replanning — revising the plan when a step returns something unexpected — is what separates a resilient agent from one that marches confidently down a plan built on a now-false assumption.
Ambiguity handling is a design decision, not an accident: an agent can ask a clarifying question, pick the most reasonable interpretation and proceed, or spawn parallel exploratory paths. Which is right depends on the cost of being wrong versus the cost of stopping to ask.
Full breakdown & examples → 1.2 Task Decomposition and Planning
The dominant production pattern is orchestrator–subagent: a lead agent holds the overall goal and coordinates, while subagents each get a narrow, well-scoped task and their own isolated context window. Isolation matters — a subagent that inherits the entire parent transcript wastes tokens and dilutes its own focus.
Three topologies come up repeatedly: hub-and-spoke (one coordinator, many independent subagents reporting back — the most common and easiest to reason about), pipeline (each agent's output is the next agent's input, good for staged transformations), and peer-to-peer (agents communicate directly with each other — powerful but much harder to keep predictable and to debug).
Handoff design is where most orchestration bugs live: what exactly does a subagent return to the coordinator, in what format, and how much of it survives versus gets discarded? A subagent that returns a wall of raw output instead of a condensed, structured summary defeats the purpose of isolating it in the first place.
Full breakdown & examples → 1.3 Multi-Agent Orchestration
Agentic systems fail in characteristic ways: looping on a task that can't succeed, taking an action based on a hallucinated premise, or silently treating a failed step as if it had succeeded. Mitigation is layered — timeouts and step limits catch the first, grounding and verification steps catch the second, and explicit success criteria catch the third.
Checkpoints pause the agent before high-consequence or irreversible actions (sending an email, deleting data, committing a purchase) and require explicit approval before continuing. Where to place a checkpoint is an architectural decision that trades safety against friction — too many and the agent is barely autonomous; too few and a bad decision compounds before a human ever sees it.
Monitoring an agentic system means more than logging final output: it means capturing the intermediate steps, tool calls, and decisions so a failure can be diagnosed after the fact, and building evaluation sets that catch regressions before they reach production.
Full breakdown & examples → 1.4 Reliability and Human Oversight