AI agent providers are the model vendors behind your agents, like OpenAI, Anthropic, or Google. Choose each one by the job it does. Quality, cost, hosting. A second vendor won’t give you a second agent, though. Separate roles, tools, and coordination will.
Lots of demos blur this. One prompt goes to two models, somebody merges the answers, and the slide says multi-agent system. It’s a routed request. This guide walks through what the real thing takes, in five steps.
Providers Versus Agents
Start by pulling four jobs apart, because they get lumped together all the time.
Orchestration decides what runs next. Messaging carries data between components. Model execution is the request that goes out to a vendor and comes back with text. Application state is where workflow data sits while the work is in progress.
A provider only does the third one. Your app or your platform handles the rest.
So what’s an agent? Think of it as a role. It gets its own instructions, its own tools, its own context, and something has to coordinate it with the other roles. OpenAI’s Agents SDK guidance gives sensible reasons to split one agent into several: a different tool set, a different approval policy, a different model, or cleaner routing in traces. Notice that vendor count never shows up.
That’s why one model can power three agents. It’s also why three vendors can sit behind one agent that just loops on itself. Count roles. Ignore logos.
Five Steps to Combine Providers
One hypothetical example runs through all five steps. It’s invented, not a customer story. Say you’re building a pipeline that turns a changelog into reviewed release notes.
Step 1: List the jobs
Write down every task. No vendor names yet, they’d only bias you.
Ours has three jobs: work out what changed, write the notes, check each claim. Next to every job, jot its input, its output, the tools it needs, and how sensitive its data is. That last note pays off in step 3, when hosting rules start crossing options off.
Step 2: Draw the boundaries
Take each job and ask whether it needs different tools, a different approval rule, different context, or a different model. If every answer is no, fold the job into its neighbor.
Sometimes that leaves you with one agent. Good. Anthropic’s December 2024 guide on building effective agents says to find the simplest workable solution and add complexity only when you must. A single agent looping over its tools is a perfectly respectable design.
Our pipeline does split, though. Gathering needs a search tool. Writing must never touch the web, since we want drafts grounded in findings only. Checking needs a hard rule: nothing passes without support. Three real boundaries, so three agents. The researcher searches a knowledge base of changelogs and saves what it finds. The writer drafts from those findings. The reviewer returns a pass or a list of fixes.
Step 3: Pick a provider per role
Vendors enter the picture now. Compare candidates on answer quality for the task, latency, price per call, and context length. Then check where your data may run.
Hosting often decides before quality even gets a vote. A cloud contract might force AWS Bedrock or Azure AI. Sensitive records might need a local model. Cross those options off first.
After that, shortlist two candidates for each role and run identical tasks through both. Score the outputs yourself. One habit I’d recommend: begin with every role on a single provider and switch a role only when your numbers say so. In our example the researcher might want a search-capable model, the writer a long-form one, the reviewer a cheaper one. Those are guesses until tests back them.
Step 4: Wire the coordination
Agents won’t coordinate on their own. You decide the order, what each one hands over, and what happens on failure.
Ours runs researcher, then writer, then reviewer. Fixes come back? The writer gets one more pass, and then the run stops. That cap matters more than it looks. A stubborn reviewer with no limit can eat your budget overnight.
Pick a home for shared data too. Findings, drafts, and review notes all need a spot every agent can read. And decide which of it must survive a crash, because that part is on your application. More on that below.
Step 5: Test and trace
Test each role alone before you test the chain. Hand the reviewer a draft with a planted false claim. Does it catch it? Then run the whole pipeline end to end.
In production you want one trace per run. Which agent ran, which model it used, how long it took, what it cost. Skip that and a mixed-provider setup becomes guesswork the first time quality drops.
What DNotifier Handles
DNotifier provides one SDK and one API with multi-model support. In this pipeline it covers orchestration, model routing, messaging between services, and observability. Your app and your providers keep the rest.
Here’s how that lines up with the steps.
For step 3, you save provider connections in the portal, then name a provider and model in code. The multi-model docs say the SDK never needs your provider secrets. Eight providers are listed: OpenAI, Anthropic, Google Gemini, Hugging Face, Perplexity, Azure AI, AWS Bedrock, and Ollama.
For step 2, each role becomes a defineAgent call with a name and, if you like, its own provider and model. A model set on an individual call beats the agent’s setting, and the agent’s setting beats the project default. One tutorial in the docs pairs Perplexity for research with Claude for writing, which is close to our hypothetical.
Step 4 happens in a workflow entry function. It calls runAgent for one step, or runAgents to run several at once, and agents pass data through ctx.state. The reviewer could live on another server, registered by receiverId. Remote agents do need a WebSocket transport.
Step 5 is observability: true. Turn it on and the dashboard shows agent steps and model call steps, with latency and optional token usage. The docs add that steps can show which model ran.
Plenty stays with you. Provider accounts, contracts, rate limits. Prompt quality and evaluation. Fallback rules. The docs describe ctx.state as shared across a single run, and they don’t mention durable checkpoints or automatic retries. So keep long-lived state and retry logic in your own application. DNotifier doesn’t replace your data stores or every framework you already run, either.
Now the trade-off. Every extra agent or provider adds a hop, a bill, and another place to fail. Vendors also differ on prompt behavior, tool-call formats, rate limits, and data terms. That’s our architectural reading, not a measured result. A multi-agent design earns its cost only where the boundaries are real.
FAQ
Can one model power several agents?
Yes. Role, tools, context, and coordination define an agent, not the model underneath. Same model with different instructions and tool access gives you distinct agents, once something coordinates them.
Do I need several providers for a multi-agent system?
No. Add a second provider when a role needs a capability, price, or hosting option the first can’t give you.
How do I compare providers fairly?
Run identical tasks, prompts, and tools through each candidate. Score the outputs on your own criteria. Track latency, cost, and failures per agent step.
Count roles, not vendors, and match each provider to the work its agent does. If you want provider connections, workflows, and observability behind one SDK, have a look at http://www.dnotifier.com.
