- Multi-agent orchestration patterns and best practices
- AI agent governance and identity management
- Coordinating specialized agents across systems safely
Almost anyone can set up a powerful AI agent in the space of an afternoon, and in many enterprises, various teams are doing just that. But without coordination, each agent arrives wired to its own copy of the data, on whatever platform the team that built it happened to prefer. No two agents answer the same question the same way, and none of them notice the difference. Work gets duplicated, policy drifts, and the silos your integration program spent years dismantling reappear under new names.
Continuing in this direction denies organizations the real benefits of agentic AI: turning single-user productivity tools into what Gartner senior director analyst Anushree Verma calls “platforms enabling seamless autonomous collaboration and dynamic workflow orchestration.”
While just over a quarter of organizations aspire to run multi-agent AI systems, only 9% have actually succeeded.
So, how can you make coordination between specialized agents reliable and safe enough to let them start acting on your production systems?
Why One Agent Isn’t Enough
Ask a single agent to cover three business functions at once, and its accuracy across all of them degrades. Every task you add forces the agent to choose from more tools, hold more context, and deal with more exceptions nobody wrote instructions for. The model ends up spending its reasoning budget on deciding how to do a task instead of completing it.
The natural solution is to split that work across separate agents. Although that fixes the accuracy problem, you inherit a new one: juggling a fleet of agents.
Traditional integration can’t help much here, because AI breaks two of the assumptions integration was built on:
Agents are non-deterministic, so the same input can produce a different result on Tuesday morning than it did on Monday afternoon, breaking the identical-input, identical-output principle that point-to-point wiring depends on.
Unlike ordinary software, agents don’t wait to be called. They are active participants in your operations, roaming across your systems, calling APIs, starting workflows, and editing your records, which breaks the expectation that a component acts only when something invokes it.
Connect non-deterministic, proactive agents point to point, and you recreate fragmentation with far higher stakes, pushing risk onto downstream systems that were never designed to absorb it.
Multi-agent workflows need an intelligent coordinating layer to run safely and smoothly, and this is what separates the AI agent front-runners from everyone else. KPMG reports that 24% of the organizations it classifies as “AI leaders” are already orchestrating AI across their workflows, 39% are creating or rolling out multi-agent systems, and 38% are scaling AI to include more functions. That raises the obvious question: how are they doing it?
What Is Multi-Agent Orchestration?
Multi-agent orchestration places a coordinating layer above your specialized AI agents to ensure they operate as one system under a single set of controls, working toward an outcome no single agent could reach alone. It routes each piece of work, carries context from one agent to the next, holds the state of the process, and decides what happens when a step fails.
A single specialized agent operates in one defined role, manages the tools it needs, and has permissions scoped no wider than required for the task. An agent that only reads invoices can be tested against those and nothing else, and when it does something you didn’t expect, you can see exactly which tools and records were available to it.
On its own, that agent runs a closed loop. It plans its sequence, calls its tools, and stops when it judges the goal is met, holding the context for that run and nothing beyond it. That makes it a good fit for work that can stay inside one system under one set of permissions.
Run a dozen closed loops like this across your business, and the cracks between them start to show. Take order-to-cash as an example. Your credit check agent verifies the customer, then your fulfillment agent starts cold and verifies the same customer again, because nothing carried the result across. An exception on a shipment gets picked up by both the logistics agent and the customer service agent, or by neither, since one doesn’t know the other exists. When the invoicing agent fails partway through, the agents waiting on its output never find out, so the order stops moving, and nobody knows about it until the customer calls. And if you ask later why that order took nine days to complete, you’ll discover that there’s no single record to explain it all, only a dozen partial ones.
With a coordinating layer, you can sidestep all these pitfalls at once, thanks to:
- Shared context: the verification your credit check agent completed now travels with the order, and the fulfillment agent can read the result instead of repeating the lookup.
- Routing: the layer reads the shipment exception, matches it to whichever agent owns that class of problem, and hands it over once, so the work has a named owner from the moment it arrives.
- Recovery: when your invoicing agent fails, the layer catches it and chooses a retry, a different agent, or an escalation to a person, so the order keeps moving instead of getting stuck unnoticed.
- One records: every routing decision, handoff, and failure lands in one record, so the question of why something went wrong has one answer rather than a collection of fragments.
4 Core Patterns for Coordinating Multi-Agent Workflows
Agents are diverse and unpredictable, but the ways they coordinate come down to a small, repeatable set of patterns. Choosing the right one for each workflow is a key design decision because changing it after deployment is costly. Here’s how each one works:
1. Supervisor-worker (hierarchical)
Central control is the main point here, so this fits goals complex enough that a single component has to own the outcome. One agent takes charge: it breaks down the goal, hands each part of the work to a specialist, reviews what comes back, and assembles the answer.
For example, a lender running loan origination this way delegates the credit pull, the document check, and the risk score to three agents working in separate systems of record, then has the supervisor read the results together and reach a decision none of them could have made separately.
Keep an eye on the supervisor itself, because it can throttle throughput under load and take everything else down with it when it fails.
2. Sequential (chain)
In this pattern, each agent’s output becomes the input for the next in an order determined before the process runs. It suits work with real linear dependencies.
Contract intake is an everyday example: one agent extracts the terms from your document store, a second tests them against your policy library, and a third writes the summary into the customer relationship management (CRM) record. Now, your legal reviewer opens the file already knowing which clauses need attention.
The caveat is that an error introduced at step one travels the full chain unchallenged. The fix is to include validation between the steps rather than only at the end.
3. Parallel (concurrent)
This approach sees independent subtasks run at the same moment, and their results get merged afterward. This is useful when analyses don’t depend on one another or when latency is critical.
A supplier risk assessment can read enterprise resource planning (ERP) records, logistics feeds, and public filings at once, and the orchestrator combines the three results into one risk score your procurement team checks before renewing the contract.
Disagreement is the common failure mode here. The remedy is to settle the reconciliation rule before deployment, whether that’s confidence scoring, a majority vote, or a dedicated tie-break agent.
4. Dynamic handoff (runtime routing)
This pattern applies to work where nothing about the task’s path is decided in advance. A triage agent reads each request as it arrives and routes it to whichever specialist handles that class of work.
In practice, a billing question goes to an agent on the finance stack, a technical fault to one working the IT service management queue, and anything ambiguous to a human.
Because misroutes and routing loops are a real risk, you’ll need to cap the number of handoffs allowed and build the escalation to a human into the design rather than patching it in later.
How Specialized Agents Coordinate Across Systems
Whichever pattern you choose, you’ll need to decide which agent takes each request, move context from one agent to the next, give them a common way to reach your ERP, CRM, and ticketing queue, and keep a record of what every agent did. Four things keep the pattern running in production:
Intelligent routing
The simplest way to route work is a lookup table you write yourself: refund requests to the billing agent, outages to the incident agent, everything else to a person. That works until you have more than a handful of agents, at which point each new specialist means another edit, and whoever maintains the table becomes the bottleneck for every agent you add.
A coordinating layer routes without a table, matching each subtask to the most appropriate agent using the role and description it publishes about itself, so a new specialist starts receiving work as soon as it’s registered and described accurately.
Shared state and memory
When an agent finishes a run, the task moves ahead, but whatever context it worked out stays behind: the customer it verified, the exception it judged safe to ignore, the reason it picked one supplier over another. The next agent starts from the request alone, so it asks your customer for an order number they supplied two minutes ago or repeats a paid lookup someone already ran.
To solve that problem, you need three tiers of memory: short-term context for the task in progress, long-term knowledge that persists across sessions, and process state recording how far the work has come. With all three, your second agent can resume where the first stopped, and nobody has to repeat themselves.
Communication and interoperability
Connect just five agents to eight systems by hand, and you end up maintaining 40 connections, each with its own authentication and payload shape, all needing an engineer who remembers why they break.
The Model Context Protocol (MCP) replaces that arithmetic with one interface. Using this open standard, often described as “USB-C for AI”, a system publishes what it exposes once, and from then on any agent can discover those tools and call them without a build.
Agent-to-agent (A2A) protocols cover the connections MCP doesn’t, standardizing how one agent hands work to another rather than how an agent reaches a tool. So an agent your CRM vendor shipped can pass a task to yours without either side building against the other.
Monitoring and observability
A failed multi-agent workflow might look like five agents reporting success and one unhappy customer whose order never shipped. Even once you notice the problem, the cause can be challenging to chase down. Maybe each agent logged its run as finishing cleanly, but the fault occurred in a handoff between two of them, and no single log covers that ground.
A distributed trace links every agent that handled the request into a single record, so you can pin the failure to the step that caused it instead of taking your whole stack apart.
What’s more, if you capture token consumption, cost per workflow, latency, and success rates per agent in the same place, you can see which agents cost the most as well as which ones are going wrong.
DIY Frameworks or Governed Platforms?
That leaves the implementation question: routing, memory, communication, and monitoring.
Code-first frameworks like LangGraph, Microsoft’s Agent Framework, CrewAI, and Google’s Agent Development Kit (ADK) hand you the primitives and expect you to assemble them. They let you define agents and write the code that connects them, which is worth doing when your requirements are unusual enough that a product’s defaults won’t work.
The alternative is a governed enterprise platform. You give up some of that flexibility, but the strongest platforms come with the coordination machinery already assembled, along with the identity, audit, and access control that a framework requires you to supply yourself.
Either route leaves you owning a governance layer, because neither one defines what your agents may do once they’re acting on production systems.
Governance, Identity, and Access Control
You have to put controls in place before the agents make their first moves. If you have a multi-agent orchestration layer, it becomes the one component every agent passes through, so you solve identity, access, oversight, and audit once, in that layer, instead of over and over across every platform you deployed on.
But what happens when enterprises introduce agents without orchestration?
An AI chatbot that returns a wrong answer might cost someone an hour. When AI agents make mistakes, the consequences cut much deeper. An agent might issue a refund that wasn’t owed, release a shipment that should have been held, or update a payroll record for the wrong employee. And because agents are non-deterministic, they might slip up on one run but not the next, making such failures hard to catch early and frustrating to trace.
Barriers to scaling
High-profile deployments frequently falter once the bill arrives. KPMG finds that 49% of organizations have delayed or scaled down agent initiatives after costs outran the value returned, and the expenses that do the damage are typically the ones nobody can attribute to a particular agent: model calls placed twice because two agents covered the same ground, reruns forced by stale data, engineering hours spent working out which of 50 agents produced the wrong answer.
The problem is that none of that is visible without a layer that catches it. Deloitte puts the share of companies with a mature model for governing autonomous agents at just 21%. Yet 85% plan to keep customizing agents to meet their business needs, growing their fleets while the controls stay on the shelf. Getting those unexpected costs onto a line item where someone can act on them starts with knowing which agent triggered them.
Agent identity and oversight
Provisioning a single credential takes little time and effort, while per-agent identity means maintaining a growing inventory. That’s why agents deployed outside a multi-agent orchestration layer tend to share one service account. The downside is that dozens of agents then authenticate as the same user, and when something fails (say, a record that should have been left alone gets deleted), the log names only the service account, which does nothing to narrow the list of suspects. Revoking one agent’s access often means revoking it for all of them at once.
Your ever-expanding collection of agents rarely comes from one place, either: some ship with your integration platform, some come from partners, some come straight from a cloud model service, each with a console of its own and a different idea of what an agent is.
Part of the answer is to give each agent its own traceable identity, just the way you would a new employee. That lets you audit what each agent did, review the rights it holds, and shut one down without disturbing the rest.
You should also implement guardrails to constrain what an agent may attempt, and add a human-in-the-loop checkpoint for actions in which a wrong call costs more than a short delay for review.
Deciding which actions need that checkpoint is becoming a larger part of agent wrangling. With digital assistants set to make up 36% of core technology teams by 2027, your people will soon move from doing the grunt work to supervising the agents that take it on.
The authentication problem
Ask an HR agent what the chief executive earns. If it reaches the workforce system through a single service account provisioned to cover every request, it may well answer. That’s the kind of shortcut that can produce a working agent in an afternoon and multiple regulatory breaches sometime down the line. A common stopgap is to add an instruction to the prompt telling the model to refuse inappropriate or illegal requests, but that holds only until someone asks a question in a way the prompt didn’t anticipate.
A prompt is simply guidance the model weighs against everything else in its context. What you need instead are permissions on the target system that enforce policy before the system returns anything.
Delegated authorization gives you that enforcement by borrowing the approach your access controls already apply to people. With it, each call runs under the identity and permissions of the person the agent is acting for, with OAuth 2.0 and your single sign-on provider carrying that identity through. Your workforce system decides what that person can see, and the model never has to judge sensitivity for itself. Role-based access control (RBAC) and least-privilege scoping then govern agents on the same terms as they govern your staff.
Your agent’s own traceable identity and the end user’s delegated identity do different jobs, and you need both. The first answers who acted; the second decides what that action was allowed to reach. Assign both identities agent by agent, and you’ll be reimplementing them for years. Manage them at the orchestration layer, and every agent inherits them at once.
Boomi Orchestrates Agents Across Every System
Connectivity with trusted data underneath, recovery when a step breaks, one registry across providers, identity that survives a handoff, and cost reporting per agent all point to one solution: a governed layer between your agents and the applications they act on. The Boomi Enterprise Platform provides everything you need up front, in one system, instead of bolting on disparate solutions once the gaps appear. Boomi delivers:
- Boomi Connect: more than 1,000 pre-built managed connectors with OAuth flows, token refresh, and per-user identity handled centrally, giving each agent the permissions of the person it’s working for and nothing beyond them.
- Boomi MCP: a vendor-agnostic catalog of MCP servers with audit trails and lifecycle controls, exposing your tools to any model without tying you to one provider.
- Boomi DataHub: master data reconciled into golden records, which your agents reason over in place of whichever copy they reached first.
- Boomi Agent Step and supervisor agents: a way to call one agent or several inside an integration process, where a supervisor agent generates the execution plan, assigns tasks to collaborator agents, and routes by context. The supervisor-worker pattern becomes a configuration, not a custom build.
- Boomi Flow: low-code process orchestration with error handling that routes an exception to the person who should rule on it. A broken step turns into an approval waiting in someone’s queue instead of an order sitting where nobody can see it.
- Boomi Orchestrate: outcome-driven assembly. You describe what you want in plain language, and the platform builds the blueprint behind it, covering agents, integrations, APIs, event streams, and data models. It then delivers the whole multi-agent workflow as one governed process.
- Boomi Agent Control Tower: a single control plane that registers, monitors, and governs agents across Boomi and third-party providers such as Amazon Bedrock, Salesforce Agentforce, Microsoft Copilot, and Snowflake Cortex. It adds trust levels, real-time anomaly detection, and complete activity and session logs.
- Cost and consumption visibility: token spend, invocation counts, and cost per workflow tracked agent by agent. Repeated model calls and reruns after a bad output land against the agent that incurred them instead of disappearing into one unexplained invoice.
Boomi’s customer base now runs 50,000-plus AI agents, over 200 million integration patterns inform the platform’s recommendations, and more than 300,000 endpoints are already exposed as MCP interfaces. In June 2026, Boomi was named a Pioneer in the inaugural Gartner Emerging Market Quadrant for No-Code Agent Builders, an evaluation based on governance, enterprise connectivity, and multi-agent orchestration.
Putting agents to work across your business isn’t a matter of just adding more agents. It requires one governed layer. Learn more about why agentic AI demands a new approach to integration and orchestration.