Single agents hit a ceiling fast. Real production workloads need multiple specialized agents working together -- researching, writing, reviewing, deploying. Here is how to coordinate them without building a distributed system from scratch.
A single LLM agent calling tools sequentially is fine for simple tasks. But complex workflows -- "research this topic, write a report, fact-check it, format it as a PDF, and email it" -- require different capabilities, different contexts, and often different models. Trying to cram all of that into one agent's context window leads to confused reasoning, blown token budgets, and unreliable results.
Multi-agent orchestration splits work across specialized agents, each focused on what it does best. The orchestration layer handles routing, state sharing, and result aggregation.
Agent A produces output that becomes Agent B's input. Linear, predictable, easy to debug. Use Slopshop's pipe endpoints to chain tool calls without intermediate code. Best for: content pipelines, ETL workflows, document processing.
A coordinator agent dispatches subtasks to N worker agents running in parallel, then aggregates results. Slopshop's Army Mode runs up to 1,000 agents simultaneously with built-in result collection. Best for: research tasks, data analysis, bulk processing.
Multiple agents operate in the same persistent workspace with shared memory, files, and task queues. Agents can read each other's outputs and build on them. Slopshop Hive workspaces provide this out of the box. Best for: collaborative writing, code generation, ongoing projects.
A supervisor agent decomposes tasks, assigns them to workers, reviews results, and re-assigns if quality is insufficient. Workers are stateless; the supervisor holds all context. Best for: quality-critical workflows, customer service, code review.
The biggest challenge in multi-agent orchestration is not routing -- it is state. Agents need to share data without stepping on each other. Traditional approaches require you to set up Redis, define serialization formats, and handle race conditions.
Slopshop solves this with free persistent memory that all agents on the same API key can access. Use key prefixes as namespaces (research:, draft:, review:), queues for task distribution, and counters for progress tracking. All atomic, all free, all persistent.
The fastest path is to start with a simple pipeline and add complexity only when you need it. Here is a practical starting point:
Check the playbooks for pre-built orchestration templates you can deploy immediately.
500 free credits. Shared memory included. No infrastructure to manage.