Multi-Agent Workflows: How to Chain AI Agents Together for Real Work
Why one agent doing everything underperforms, how to split work across specialised agents, and how to design dependencies, failure handling, and schedules that hold up.
The first thing most teams build is one agent that does everything. It has twenty tools, a two-page system prompt, and instructions covering six different jobs. It works in testing and gets unreliable in production.
The reason is not model capability. It is that the instructions conflict. "Be exhaustive when researching" and "be concise when writing" are both correct, and holding them at once makes the agent worse at each.
Splitting the work across several agents, each with one job, fixes this. This guide covers when to split, how to design the graph, and the operational details that decide whether a workflow survives contact with real data.
When one agent is enough
Not everything needs a workflow. One agent is the right answer when:
- The task is a single job, even a complex one
- The tools all serve the same purpose
- The output is one thing
- You can describe the job in a paragraph without using "then"
Reach for multiple agents when the word "then" keeps appearing, when different stages need genuinely different tone or tooling, or when one stage should be able to fail without killing the rest.
Splitting the work
Split by job, not by step size. A useful test: could you hire a different person for each stage without it being strange? If yes, those are separate agents.
A typical content pipeline splits cleanly:
| Stage | Agent | Why it is separate |
|---|---|---|
| Research | Web and data tools, told to be thorough | Wants breadth, and has a different failure mode |
| Analysis | No external tools, told to be critical | Reasoning over gathered material, not collecting more |
| Writing | No external tools, told to be concise | Tone instructions that would hurt research |
| Delivery | Email or messaging tools only | Side effects, so it needs tight tool scope |
Note that only two of the four have external tools. Restricting tools per stage is one of the main benefits of splitting, because it makes the stage with side effects the narrowest one.
Designing the graph
A workflow is a directed graph. Connections say what runs after what. Everything a step depends on is passed to it automatically, so you write each prompt as if the upstream work is already in front of it.
Three properties follow from the shape of the graph:
Independent steps run at the same time. If you are gathering from four sources, make them four steps with no connections between them. They run in parallel and the analysis step waits for all four. Chaining them in a line instead makes the workflow four times slower for no benefit.
A step with several inputs waits for all of them. This is how you build fan-in: gather from many places, then converge on one analysis.
Cycles are not allowed. Arkios rejects a connection that would create a loop as you draw it, so a workflow cannot deadlock. If you want iteration, use the per-step iteration cap rather than a loop in the graph.
The shape most workflows want is a diamond: fan out to gather, converge to analyse, then a line through writing and delivery.
Writing step prompts
Each prompt covers one stage only. The most common mistake is restating the whole workflow in every step, which confuses the agent about what it is responsible for.
Write the instruction for this step, and refer to incoming work naturally:
Read the competitor findings above. Identify what changed since last week and what it means for our positioning. Return at most five points, ordered by how much they matter.
Two things make prompts more reliable:
Say what the output should look like. "At most five points", "one page", and "a table with a row per competitor" all reduce variance considerably.
Add success criteria. Arkios lets you attach a plain-language description of a good result to each step, for example "output has a price for each competitor." The step is checked against it before the workflow continues, which catches a failed research stage before three more steps build on nothing.
Failure handling is the real design work
Getting a workflow to succeed once is easy. Getting it to behave sensibly when something goes wrong is the actual engineering.
Set failure behaviour per step according to what that step does.
Read-only steps should retry and continue. If one of four sources is down, you still want a brief from the other three. Continuing on failure is right here.
Analysis steps should retry and then fail. There is no point writing up an analysis that did not happen.
Steps with side effects should retry once, then stop. Sending, posting, and creating records must not run twice. One retry covers a transient network error. Anything beyond that needs a person.
Cap iterations everywhere. Each step should have a ceiling on how many tool-calling rounds it may take. A research agent that finds an endlessly paginating source will otherwise keep going until something else stops it.
Scheduling
Once a workflow is reliable, put it on a schedule. Arkios supports manual runs, repeating schedules, one-time scheduled runs, and API triggers.
Two practical notes:
Schedule for when the output is needed, minus the run time. A Monday 9am standup brief should run at 7am, not 8.55am.
Notify a person, at least at first. Notifications can fire on every run or only when a condition is met. Start with every run and move to conditional once you trust it.
Watching runs
Every run keeps its status, duration, and per-step output. This is what makes a workflow debuggable months later, when it produces something odd and you need to know which step changed behaviour.
Two habits worth forming:
Read the first few runs end to end. Not just the final output. The middle steps are where drift starts.
Check runs that succeeded but took unusually long. A step that used to take 20 seconds and now takes three minutes is usually looping near its iteration cap, which means something upstream changed.
Workflows that improve
A workflow that makes the same mistake every week is a maintenance burden.
When a step in Arkios fails for a behavioural reason, such as a wrong assumption, a missing instruction, or the wrong tool for the job, the workflow distils what happened into a lesson and carries it into later runs. Infrastructure failures like timeouts and rate limits are excluded on purpose, because they say nothing about how the agent should work and would only add noise.
You can also write notes directly into workflow memory. A note applies to every future run, which is the quickest way to correct a recurring problem without editing several prompts.
Common mistakes
Too many steps. Six focused steps beat fifteen tiny ones. Every step adds latency and a place to fail.
Sequential steps that could be parallel. Check whether each connection is a real dependency or just the order you happened to think of them in.
Giving every step every tool. Scope tools per step. It improves reliability and limits blast radius.
No human in the loop on the first version. Have the workflow produce and notify before it produces and sends.
Automating a process nobody has written down. If the manual version is undocumented and inconsistent, the automated version will be too. Write it down first.
Where to go next
- Workflows documentation: full setup, scheduling, and memory
- How to Automate Your Daily Work with AI: finding the right first task
- Agents Overview: building the agents each step uses
- Tools and Connectors: MCP servers and custom tools