From a single agent thinking step-by-step, to fleets of specialized agents collaborating at scale — explained with live animations.
A regular AI model takes input and returns output. An agent goes further — it can use tools, check results, and keep going until the job is done.
The most common pattern is ReAct (Reason + Act). The agent alternates between thinking about what to do next and acting by calling a tool.
Each cycle is a step. Steps continue until the agent decides it has a final answer or hits a maximum step limit.
Tools might include: web_search code_interpreter file_read api_call
Every agent, no matter how complex, is built from four components working together:
Model — The LLM that reasons and decides. It reads the context and decides what action to take next.
Memory — What the agent remembers. This ranges from the short-term conversation window to long-term vector databases.
Tools — The agent's hands. Functions it can call to interact with the world: search engines, code runners, APIs.
Instructions — The system prompt. Defines the agent's persona, goals, constraints, and how to use its tools.
When the model decides it needs a tool, it emits a structured tool call — a JSON object naming the tool and providing arguments. The runtime intercepts this, executes the function, and returns the result.
The result gets added back into the context as a tool result message, and the model continues reasoning with this new information.
This design means you can give an agent access to powerful tools while controlling exactly what each tool can do.
A single agent has hard limits: its context window fills up, it can only do one thing at a time, and mixing too many responsibilities degrades quality.
Multi-agent systems solve this by splitting work across specialized agents that coordinate with each other.
The benefits: parallelism (multiple agents work at once), specialization (each agent is an expert at one thing), and scale (unlimited context via handoffs).
Agents communicate by passing messages. An orchestrating agent calls a sub-agent just like it would call any other tool — by emitting a structured request.
The sub-agent receives the request, runs its own reasoning loop, and returns a result. From the orchestrator's perspective, this looks exactly like a tool call result.
This agent-as-tool pattern is powerful: you can nest agents arbitrarily deep, composing complex systems from simple building blocks.
A central orchestrator agent plans the work and delegates tasks to specialized worker agents. Workers report back; the orchestrator assembles the final result.
The orchestrator never does the "real work" itself — it focuses on planning, decomposition, and synthesis. Workers focus on execution without worrying about the big picture.
Best for: Research pipelines, content generation, multi-step data tasks where work can be parallelized.
planning delegation synthesis
Agents are chained like an assembly line. Agent A's output becomes Agent B's input, which feeds Agent C, and so on. Each agent transforms the data before passing it forward.
There's no central coordinator — each agent just receives input, does its job, and hands off to the next.
Best for: Document processing, content workflows, ETL pipelines where each stage has a clear responsibility.
sequential transformation modular
A dispatcher sends the same task (or parts of it) to multiple agents simultaneously. All agents work at the same time. A reducer waits for all results and merges them.
Instead of processing 10 documents one by one (slow), fan-out processes all 10 at once — then combines the results.
Best for: Batch processing, competitive analysis across many sources, parallel document ingestion.
parallel speed aggregation
Like a management tree. A top-level strategic agent breaks work into sub-goals and delegates to mid-level managers, who in turn spin up worker agents for execution.
Each tier only knows about its immediate reports and manager. This keeps the context clean and makes very large tasks tractable.
Best for: Enterprise automation, long-running projects, systems with thousands of sub-tasks.
scale delegation isolation
Two dedicated agents: a Planner that creates a full task plan upfront, and an Executor that carries out the plan step by step.
Unlike ReAct (which interleaves planning and acting), Plan-and-Execute commits to a full plan first. This produces more coherent, consistent multi-step work.
Best for: Complex research tasks, software projects, long documents where consistency across steps matters.
planning execution consistency
The agent (or a separate critic agent) reviews its own output and identifies flaws before declaring the task done. The critique feeds back into a revision cycle.
This dramatically improves output quality: the generator focuses on creation, the critic focuses on flaws, and the cycle repeats until quality is acceptable.
Best for: Writing, code generation, analysis — any task where quality matters and first drafts are rarely perfect.
critique revision quality
Multiple agents independently produce answers, then challenge each other's reasoning in rounds of debate. A judge (or consensus mechanism) picks the winner or synthesizes the best answer.
Debate reduces single-model hallucinations and blind spots — an error that one agent makes is likely to be caught by another.
Best for: High-stakes decisions, factual verification, medical/legal analysis where accuracy is critical.
adversarial consensus accuracy
All agents read from and write to a shared workspace (the "blackboard"). There's no central orchestrator — agents subscribe to changes and activate when relevant new data appears.
Any agent can contribute facts, hypotheses, or partial results. Others build on top. The system converges on a solution organically.
Best for: Complex problem-solving, scientific reasoning, systems where multiple expertise domains need to collaborate without a fixed sequence.
shared memory event-driven emergent
The agent pauses at critical decision points and requests human approval before continuing. This is essential for irreversible, high-risk, or high-cost actions.
HITL doesn't mean humans do the work — they just act as checkpoints for decisions that shouldn't be fully automated.
Common HITL triggers: before sending an email, before executing a financial transaction, before deleting data, or when confidence is below a threshold.
approval oversight safety
Every LLM has a finite context window. In long-running agent tasks, you must proactively manage what's in that window — keeping it relevant, trimming the stale, and summarizing where needed.
Strategies include rolling summaries (compress old turns), RAG (retrieve only what's relevant), and memory tiers (hot/warm/cold based on access frequency).
Context boundaries also define what one agent knows vs. another. In multi-agent systems, each agent should only receive the context it needs for its specific role — this is the principle of least privilege for context.
context window RAG summarization memory tiers
Production agent systems require more than just the agents themselves. They need an entire supporting infrastructure:
Observability — Every agent action, tool call, and LLM response should be traced. You need to know why an agent did what it did.
Guardrails — Input and output validation, content policies, rate limiting, and cost controls prevent runaway agents.
Auth & Permissions — Agents must operate under scoped credentials. An agent should never have more access than required for its task.
Evals — Automated test suites that verify agent behavior doesn't regress as you update models or prompts.
observability guardrails auth evals
With 10 patterns to choose from, the hardest question is: which one fits my problem? Use these three questions to narrow it down quickly.
① Is the task a single loop or many coordinated steps?
Single loop with tool use → ReAct (P01). Multiple coordinated agents needed → continue.
② Does order matter?
Strict order, each step depends on previous → Pipeline (P03) or Plan-Execute (P06). Order doesn't matter, work is independent → Fan-out (P04).
③ How much oversight do you need?
High stakes, irreversible → add HITL (P10). Need quality checking → add Reflection (P07). Need verified facts → Debate (P08).
decision tree selection guide architecture
Most agent failures fall into predictable categories. Knowing them helps you design defenses upfront.
Infinite loops — The agent keeps calling tools without making progress. The LLM is "stuck" reasoning in circles. Fix: max step limits, loop detection, progress checks.
Context overflow — The conversation history fills the context window and earlier instructions are forgotten. Fix: rolling summaries, RAG, clear system prompt at every turn.
Tool call hallucination — The agent invents tool arguments or calls tools that don't exist. Fix: strict tool schemas, output validation, error handling that re-prompts the agent.
Cascading errors — In multi-agent systems, one bad output propagates and poisons every downstream agent. Fix: validation gates between agents, confidence thresholds, explicit error states.
loops overflow hallucination cascades
When an agent does something unexpected, you need to know exactly what happened — which LLM call produced which output, which tool was called with what arguments, how long each step took.
Traces — A tree of every LLM call, tool invocation, and sub-agent call with inputs, outputs, latency, and token counts. Think of it as a flight recorder for your agent.
Spans — Each node in the trace tree. A span has a start time, end time, and metadata. Spans nest: an LLM call span may contain tool call spans inside it.
Evals — Automated tests that run your agent against a known dataset and score the outputs. Catch regressions before users do.
traces spans evals OpenTelemetry