Multi-agent: choosing and combining patterns
Multi-agent describes collaboration, not a universal switch.
What you will learn
- Explain: a workflow has explicit structure
- Apply the idea in an example: Multi-agent: choosing and combining patterns
- Recognize limitations and verify the exercise outcome
Multi-agent describes collaboration, not a universal switch. Its value must be demonstrated against a single agent.
This is a conceptual or external lab. It does not assume the application exposes every control described.
How it works, step by step
A workflow has explicit structure
A node is a stage, an edge is a transition, and state carries results between stages. A workflow has an application-defined path, although the model may choose some branches. Agentic systems include both workflows and dynamically acting agents. Nymrio offers ReAct, Reflection, Sequential, Orchestrator, Router, Parallel and Plan-and-Execute. Each changes coordination; it does not automatically make a model an expert. More agents mean more calls and contracts between roles. Start with the simplest solution that passes your tests.
A bounded agent loop
An agent combines a model with tools, a goal and state. It observes the request, chooses a step, receives a result and decides whether to continue. A chatbot may only generate text; an agent may request an external action. Commercial definitions vary, so inspect actual control. Define success and stopping: a verified result, a maximum step count, repeated errors or an exhausted budget. Use narrow capabilities and minimal permissions. Useful autonomy means freedom within boundaries enforced by software.
Cost belongs to the whole path
Count model calls, input and output tokens, embeddings, reranking, vision and tools. Reasoning can consume billable tokens without an equally long visible response. Indexing costs and conversation costs differ. Use current prices rather than a permanent number from a course. A simple estimate is tokens/1,000,000 × price plus additional operations; application credits may have their own conversion. Compare cost per successful task, not just per call. Parallel execution can reduce latency while increasing total consumption. A budget needs a verified stopping condition.
The visual map
Follow the solid arrows for the main flow. Dashed blue arrows supply data or context; dashed pink arrows show feedback or returning results. Colors and shapes distinguish models, stores, decisions and outputs. On smaller screens, scroll horizontally to follow the entire diagram.
A complete example
Compare a single FAQ agent, Router specialists and Orchestrator delegation. If simple FAQ succeeds equally with fewer calls, the team adds cost without benefit.
Try it yourself
- Compare three architectures on ten identical requests and record unnecessary delegation.
- Record the input, source and expected outcome before running the experiment. Use only the fictional data in the example.
- Follow the diagram stages. At every step record what information is received and produced; do not confuse intermediate output with the final outcome.
- Repeat after removing necessary information or making the input ambiguous. Check whether the system clarifies, stops or invents an answer.
- Compare with the explained solution. Keep the configuration, date, result and an explanation for differences. Change one thing and retest.
An explained solution
Choose the configuration that improves task success at acceptable cost and justify roles. A successful exercise lets you show the connection between input, stages and outcome. When information is missing, a cautious answer is more useful than invented details. Compare more than style: check conditions, sources and operations too.
When it helps and what can go wrong
Composed workflows are a concept, not a confirmed free-form graph editor. Choose this approach when it improves a measured need. Keep a simple baseline and compare outcomes using identical inputs. One successful example does not establish reliability in every situation.
Check your understanding
Is multi-agent an eighth template?
No. It is an architectural category that can use several existing templates.
Does more autonomy always give a better result?
No. It also increases room for errors, costs and unnecessary actions.
What outcome should this exercise produce?
Choose the configuration that improves task success at acceptable cost and justify roles.
Words to remember
- Nod: A stage in an execution graph.
- Agent: A system that can choose steps and use tools toward a goal.
- Latență / Latency: Time until a useful result.
Sources and your next step
To prepare: W08 — Plan-and-Execute: plan, act, adjust