Do I need to design the agent team myself?

No. You describe the outcome you want in one sentence and CloudyBot assembles the roles and handoffs for you. You can refine it, but you don't have to architect it.

Multi-agent

Multi-agent AI platform: describe the job, get a team that does it.

Q: What is a multi-agent AI platform?

A multi-agent AI platform runs several AI agents that each have a focused role and coordinate to complete a larger job — for example one agent researches, another extracts data, another formats and delivers the result. CloudyBot builds this team from a plain-language description and runs it on a real browser.

Q: Why use multiple agents instead of one?

A single general agent tends to lose accuracy on long multi-step jobs. Splitting the work into focused roles with clear handoffs keeps each step reliable and makes the overall result more dependable.

One general AI loses the thread on long, multi-step work. CloudyBot splits the job into focused agents that hand off to each other — research, extraction, delivery — running a real browser on a schedule, with the result sent to WhatsApp.

Try it free — no card See the model ↓

Why one agent isn’t enough

The longer and more multi-step the job, the more a single general agent drifts.

Ask one AI to research a topic, log into three sites, extract specific data, reconcile it, and format a report, and quality degrades partway through. It loses context, conflates steps, and the output gets unreliable exactly when the task gets valuable.

Splitting the work the way a real team does — each role owning one part, with clear handoffs — keeps every step accountable. That’s the multi-agent model, and it’s how CloudyBot runs non-trivial jobs. Rolling memory and pins help context survive across turns; see Specialists in the docs for how roles map to product.

One sentence in, a coordinated team out

You don’t architect the team. You describe the outcome; CloudyBot assembles the roles.

You — one sentence describing what “done” looks like.
Researcher — finds sources and surfaces what matters.
Operator — real browser: login, navigate, extract.
Delivery — formats and sends the finished output.
WhatsApp (or dashboard) — you read the result where you already are.

Each agent has one job and passes clean output to the next. If the work recurs, the whole team runs again on schedule — you don’t reassemble anything. For chaining and recipes, see Workflows and Chaining tasks.

Single-chat vs coordinated team

Capability	Typical single agent	Coordinated CloudyBot team
Handles long multi-step jobs reliably	✗ drift / brittle	✓ focused handoffs
Maintains context across all steps	✗ overstuffed threads	✓ per-role memory
Each role is independently testable	✗ blurry middle steps	✓ clear checkpoints
Runs a real browser where needed	often skipped	✓ operator Specialists
Runs on a schedule	usually manual	✓ team reruns together
Delivers to WhatsApp	✗	✓

Built-in Specialists are the preset roles CloudyBot can hire for you once the team sketch is sound. Comparison reflects common usability differences rather than naming any single competitor.

Cloudia, the Team Builder Specialist, is the starting point for designing a multi-agent setup in the dashboard.

What a team gets used for

Research → extract → report. One agent gathers sources, another pulls specifics from each, a third writes the summary.
Monitor → compare → alert. Check several sites, diff against last run, deliver only meaningful changes — see agencies and e-commerce workloads. Pairs well with scheduled agents.
Collect → reconcile → deliver. Pull numbers from multiple logged-in tools, reconcile them, send one clean figure set.
Track → qualify → notify. Watch listings, filter to genuine matches, ping you only on the ones that count — patterns we see constantly in recruitment and real estate.

Why coordinated agents matter more as jobs get more complex

Open-ended LLMs cram every subtask into one context window — research notes, scraped tables, tentative conclusions, formatting rules — all competing for attention. Complexity grows quadratically because every new instruction can invalidate an earlier inference. Coordinating Specialists keeps each slice of work legible so handoffs behave like checkpoints instead of guesses. That posture matters most when the stakes are reconciliation, audits, launches, or any browser-heavy flow where drifting one step wastes the hour you already spent proving the earlier steps.

How this fits next to a single chatbot

Chat-style tools are great for one-off questions. Multi-agent execution is for jobs where the cost of a wrong middle step is high — compliance checks, pricing pulls, recurring research, anything that needs a browser and a handoff. If you’re still choosing a category, read AI agent vs chatbot and the 2026 AI agent comparison.

Frequently asked questions

What is a multi-agent AI platform?

Several AI agents that each have a focused role and coordinate to complete a larger job — one researches, one extracts, one delivers. CloudyBot builds the team from a plain-language description and runs it on a real browser.

Why use multiple agents instead of one?

A single general agent loses accuracy on long multi-step jobs. Focused roles with clear handoffs keep each step reliable and the overall result dependable.

Do I need to design the team myself?

No. You describe the outcome in one sentence and CloudyBot assembles the roles and handoffs. You can refine it, but you don’t have to architect it.

Can the team run on a schedule?

Yes. A recurring job re-runs the whole team on your cadence and delivers the result to WhatsApp. See scheduled AI agents.

How does pricing work?

Fixed monthly plans with hard AI Task caps — no surprise overages when you hit the limit. See pricing.