An AI agent development platform should give you five things: clear agent roles, coordination, shared state with tools, messaging, and visibility. Model access isn’t on the list. That part is easy. Skip the five and you’re left with prompts in a loop, which is hard to test and harder to run.

Here’s each piece, in the order you’ll probably need it.

Step 1: Agent Roles

A model isn’t an agent. The same model can act as a planner, a reviewer, and a writer. What separates them is the job each one has, what it gets to see, and who decides when it runs.

Sometimes one loop is plenty. A model calls itself, checks its output, tries again. Fixing a failing test works fine that way. Nothing wrong with recursion.

Distinct agents earn their place when jobs need different instructions or different access. Say a reviewer shouldn’t see the writer’s reasoning. Now you need separate contexts. Swapping prompts or providers doesn’t give you that, so it doesn’t make a multi-agent design either.

Step 2: Coordination

Someone has to decide the order. Which agent goes first? Which two can run side by side? What happens to each result?

Without a home for that logic, it leaks into your app code. Bits of it end up in route handlers, bits in queue consumers. Then nobody can read the flow. A platform should let you write it once, in one function, where you can test it.

Don’t confuse this with model execution. Execution just sends a request to a provider. Coordination decides what gets sent and when.

Step 3: State and Tools

Agents need a place to put half-finished work. The ticket, the facts they pulled, the current draft. That’s application state.

Ask a hard question here: what only has to live for one run, and what has to survive a crash? Plenty of platforms keep run state only while the run lasts. Find out before you build on it.

Then tools. Knowledge search, your own APIs, the model providers you pick. You want these wired in without being locked to a single vendor.

Step 4: Messaging

Messaging carries data between pieces. Agents, services, users. It’s a different job from coordination, which decides who acts next.

The details matter more than people expect. Does a message go to a named recipient, or out to a topic? What if the recipient is offline? Does the client reconnect by itself after a drop? Each answer decides how much reliability code lands on your plate. If the docs don’t say, don’t assume.

Step 5: Visibility

Agent systems fail quietly. A reviewer approves a bad draft. A search pulls the wrong document. The output looks fine until a customer complains.

You need a record of every agent run, model call, and search, with timing. Open one run, follow a bad answer back to what fed it. That’s debugging. Without it you’re guessing.

Sample Workflow

This one is hypothetical. A support team handles refund requests.

An intent agent classifies the request. A policy agent searches the knowledge base for refund rules. A billing agent checks the order through the company’s own API. A reply agent writes the draft. A review agent checks tone and policy before anything goes out.

The coordinator runs intent first. Policy and billing then run in parallel, since neither needs the other. Reply comes next, then review. Shared state holds the ticket, the rules, and the draft. Every step gets recorded, so a failed review traces back to its inputs.

Three things are doing different work here. The tools are the search and the order API. The roles are the five agents. The coordination is the logic that sequences them.

Where DNotifier Fits

DNotifier provides one SDK and one API with multi-model support. In the steps above, it covers coordination, messaging, and visibility. Model calls still go to the provider you choose. Long-term data stays in your application.

Per the official docs, a workflow has an entry function that calls named agents. One call runs a single agent. Another runs several in parallel and returns when all of them finish. A shared state object follows the run and comes back with the result.

Each agent can carry its own default provider and model. That’s handy, but it isn’t what makes an agent. The named role and the coordinating function do that. For retrieval, an agent can call the platform’s search mid-run.

Agents can also live on other servers. Those remote agents are reached by receiver ID over WebSocket. Switch on workflow observability and step records for agent runs, model calls, and searches show up in the Workflows dashboard.

Messaging is directed. A message goes to explicit receiver IDs, not to broadcast topics. Offline receivers don’t get live delivery.

Now the trade-off. Remote agents add a network boundary. The SDK doesn’t reconnect on its own, so retry policy is your job. The public docs also say nothing about durable replay or checkpoints. If something is critical, store it yourself.

FAQ

Is one model calling itself multi-agent?

No. Repeated self-calls share one role and one context. A multi-agent system has separate roles, separate contexts, and coordination between them.

Do distinct agents need different models?

No. One model can back every agent. Different models help when cost or speed varies by role, but the role is what defines the agent.

What stays with my application?

Retry policy, long-term storage, and tool logic. Model execution happens at whichever provider you choose.

Judge a platform by what it coordinates, isolates, and shows you. Not by how many models it connects to. If you want to see how DNotifier handles agent workflows, take a look at http://www.dnotifier.com.