Key facts
| Definition | Multiple specialized agents coordinating through messages or shared state |
| Coordination patterns | Supervisor, pipeline, debate and blackboard are the common shapes |
| When to split | Parallelizable subtasks, distinct roles, or context too large for one loop |
| Building blocks | Live function calling, agents API and 30+ models for role specialization |
| Cost shape | More agents multiply tokens, latency and failure surface |
| Frameworks | Plugsky works with agent frameworks via the OpenAI-compatible endpoint |
| Free tier | Free plan with 2 free AI models, no card required |
| Status | Chat, streaming, function calling and agents are live |
TL;DR
- Split only for parallel work or genuinely distinct roles.
- Choose one coordination pattern and write it down.
- Bound every agent: step limits, timeouts and budgets.
- Make hand-offs structured — schemas, not free-form chat.
- Measure each agent's contribution before trusting the system.
How it works, step by step
- Profile the single-agent baseline: success rate, steps, latency and cost on real tasks.
- Identify the specific failure a second agent would fix — context size, parallelism or role conflict.
- Pick a coordination pattern: supervisor, pipeline or blackboard.
- Define structured hand-off contracts between agents, with schema-validated payloads.
- Set per-agent budgets: max steps, timeout, retry policy and escalation path.
- Evaluate the system end to end and retire any agent that does not improve an outcome.
Try it yourself
Open the multi-agent workflow generator →
Signals that one agent is not enough
- Context pressure: the task needs more state than one loop can hold reliably.
- Parallelism: independent research or test streams that can run concurrently.
- Role conflict: generation and critique genuinely need different instructions.
- Tool sprawl: so many tools that selection accuracy drops.
- Auditability: a regulated workflow that needs separated responsibilities.
If none of these apply, one well-designed agent with good tools is cheaper and easier to debug.
Coordination patterns
Supervisor: one agent plans and delegates, sub-agents report back. Easiest to reason about and the usual starting point. Pipeline: fixed stages, each agent handing structured output forward — ideal for document processing. Debate: agents argue positions and a judge decides, useful for review tasks but token-hungry. Blackboard: agents read and write a shared state store, which scales well but needs locking and ownership rules.
Building it on Plugsky
Every agent is a chat completions loop with its own system prompt and tool set. Use cheap aliases for narrow roles such as classification and extraction, and stronger models for planning or synthesis. Function calling carries the hand-offs, and the agents API provides session orchestration. Because all 30+ models sit behind one OpenAI-compatible endpoint, changing a role's model is a configuration change, not a rewrite. Frameworks that accept a custom base URL work unchanged.
Costs and failure modes
- Multiplied spend: each hop adds tokens; a four-agent debate can cost many times the single-agent run.
- Error cascades: a wrong hand-off propagates; validate payloads at every boundary.
- Infinite delegation: never cap-free loops; set step and time budgets.
- Diffuse ownership: nobody is responsible for the final answer unless a supervisor owns it.
- Evaluation blind spots: system-level success can hide one agent doing all the work.
Honest comparison
| Pattern | Best for | Main cost | Debug difficulty |
|---|---|---|---|
| Single agent | Most tasks | Context limits | Lowest |
| Supervisor | Delegation with oversight | Extra planning hops | Moderate |
| Pipeline | Fixed document workflows | Stage latency | Moderate |
| Debate | Review and high-stakes choices | High token use | High |
| Blackboard | Many parallel contributors | Shared-state locking | High |
Frequently asked questions
What is a multi-agent system?
A setup where several specialized agents coordinate — through a supervisor, fixed pipeline, debate or shared state — to complete a task too large or too parallel for a single agent loop.
When should I split one agent into several?
Only when a measured failure demands it: context overflow, parallelizable work, role conflict or tool-selection accuracy. Premature splitting adds cost and failure surface.
How do agents communicate?
Preferably through structured, schema-validated payloads passed as tool results or shared state. Free-form conversation between agents is harder to validate and debug.
Which Plugsky models fit multi-agent roles?
Cheap aliases such as plugsky-micro or plugsky-lite for narrow, high-volume roles, and stronger reasoning aliases for planning and synthesis. All are reachable through one API.
How do I stop runaway loops?
Set maximum steps, wall-clock timeouts and token budgets per agent, and define an escalation path when a budget is exhausted.
Can I prototype a multi-agent system for free?
Yes. The free plan includes 2 free AI models with no card, and a 14-day full-access trial supports evaluation with stronger models before you commit.