Types of AI Agents: How to Choose an Architecture

Should one agent own a task, or should specialists handle different parts of it?

The most useful types of AI agents for a business are defined by who owns the task and how work moves between systems. One agent can handle the whole case, a router can choose a specialist, or several agents can collaborate under a defined handoff contract.

What are the main types of AI agents for business?

Lists of agent types often mix theory and implementation. A reactive or goal-based agent describes how a decision gets made. A single-agent or multi-agent design describes where responsibility lives in the application. For procurement, sales, and service teams, the second distinction helps determine the number of integrations, the access each component needs, and how staff take a case back.

Single agent with several tools

A single agent can read a vendor request, check the approved supplier directory, and draft a case summary. It owns the investigation from start to finish and chooses which tool to call next. This approach is easier to trace when one team owns the work and the same evidence matters throughout. Keep each tool narrow enough that the action, required inputs, and possible errors are easy to explain.

Start with the read operations your team already performs. When you allow a write, make it a separate operation with independent validation. For example, creating an internal draft is a different responsibility from approving an account. This distinction remains useful even when both actions appear in the same admin console.

Router and specialists

A router sends a case to the right path. It might use a fixed field such as request type, or it might classify a message when the field is missing. A specialist then handles a narrow set of cases. A vendor profile update and a new vendor registration can go to different queues. The model should be able to leave an uncertain case for staff instead of forcing it into the closest category.

Routing is valuable when different case types need different permissions or evidence. Test it separately from the specialists. If the route is wrong, a great specialist still produces a bad outcome. Record the reason for escalation and preserve the original request so staff can correct the route without asking the sender to start over.

Coordinated multi-agent system

In a coordinated system, distinct agents complete parts of a shared task. One can extract data from a supplier packet while another checks independently maintained requirements. A coordinator collects their outputs and routes the case onward. The design helps when tasks can run in parallel or when different responsibilities genuinely need separate access. It introduces more handoffs and failure modes, so the format and ownership of each handoff should be explicit.

How would you choose an architecture for vendor onboarding?

Consider a regional distributor with a purchasing team and a finance team. Purchasing identifies the product category and reviews supplier information. Finance checks whether the necessary records are present. A manager authorizes the account. Start with an agent that prepares the case packet, leaves source references, and identifies open questions. Keep vendor creation with a person while you collect examples of where the current process gets stuck.

When should you split the responsibilities?

Split the flow if one part needs access to records the other should never see. For example, the purchasing path can check product eligibility while finance receives a case ID and retrieves its own records. Keep the handoff small: a case identifier, a status, a list of evidence references, and a reason code. Passing the whole conversation around makes data exposure and token consumption harder to control.

What happens when agents disagree?

Define the source of record for each decision. If the supplier directory and an attachment identify different companies, stop and assign an owner. Set a maximum number of handoffs and define what happens when a dependency is unavailable. Test a stalled agent and a duplicate completion signal. When the case resumes, use a case identifier and current state so the system does not create a second account.

How can you compare architectures before rollout?

Replay the same requests through each version and have staff score completeness, correct routing, and the decision to stop. In a simulation of 60 vendor packets, 45 are usable on first review with a single agent and 51 are usable after introducing a separate routing step. That is 75% versus 85%, a difference of ten percentage points on this sample. Inspect the cases behind the difference and look for any new errors that could affect a vendor.

60 vendor packets in the sample 75% first-pass quality with one agent 85% first-pass quality with routing

In another simulation, the single-agent path averages five tool calls and the coordinated version averages eight. Over 60 packets, that is 300 versus 480 calls. Record latency, usage, and manual review time as well. A change can be worth it if it saves meaningful review time, even with more calls, but your measurement should account for slow or failed handoffs.

300 single-agent tool calls 480 coordinated tool calls 180 additional calls

What belongs in the decision record?

Write down the job each agent owns, the tools it can use, the data it can see, and who reviews exceptions. Include expected timing and the maximum work allowed on one case. Decide how to disable a component and how to replay a case without repeating a write. Our guide to autonomous AI agents explains what to delegate, and the Anthropic engineering guide describes common agent and workflow patterns.

How will this architecture handle a future change?

Keep business state outside the agent conversation. A purchasing specialist should be able to resume a vendor case from its ID, current status, and evidence references. If the finance system is offline, keep the purchasing work and record what finance still needs to check. This makes a change to one system less likely to force a rewrite of the entire process.

What maintenance work does the design create?

Count how many prompts, tools, and handoffs change when you add a vendor category. If several components enforce the same eligibility rule, move the rule into a tested operation where practical. Ask the team to rehearse a small update in a test environment and restore the previous version. The exercise reveals who owns each dependency and whether the added architecture is manageable.

For the next step, see how to evaluate an agent before launch.

How Dopweb helps you choose and implement an architecture

We map your business process and compare designs against the same set of real cases. We connect the approved systems, define the permissions and handoffs, and set up tests before broadening the scope. We also measure context size and retries, then help your team maintain the integrations and add new case types. Learn about AI Agents & Workflow Automation.

About the author — Patrick Chemoul, CEO of Dopweb
Patrick Chemoul leads Dopweb, an agency helping businesses with websites, software, and automation. The agency also provides AI agent integration and team training. Learn about Dopweb.

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