The first time you build two AI agents that can talk to each other, it feels slightly magical.
One agent completes part of a task.
It passes information to another.
The second agent continues the work.
Suddenly, it becomes very easy to imagine an entire company operating this way.
Sales agents talking to finance agents.
Finance agents talking to operations agents.
Operations agents handing work to customer service agents.
Specialist agents coordinating across departments without somebody manually carrying information from one system to another.
On paper, it looks elegant.
In practice, this is where multi-agent systems start becoming genuinely difficult.
Because the question is not simply whether AI agents can communicate.
Of course they can.
The more important question is whether they should.
What information should move between them?
Who decides where a task goes next?
How much context does one agent need about another agent’s work?
What happens when two agents disagree?
What happens when an agent asks another agent to perform something it isn’t authorised to do?
And what happens when twenty agents are all capable of talking to each other at once?
At that point, you are no longer building a collection of clever assistants.
You are designing an information system.
And if that information system is going to operate inside an enterprise, communication between agents has to be architected just as deliberately as communication between people, departments and software systems.
The obvious model is usually too simple
The easiest way to imagine multi-agent systems is as a conversation.
Agent A speaks to Agent B.
Agent B speaks to Agent C.
Agent C returns an answer.
It resembles a group of people passing a task around.
That can work perfectly well for a demonstration.
The problem is that enterprise processes rarely move in a perfectly straight line.
A sales process might require information from finance.
Finance may need information from compliance.
Compliance may discover something that changes what sales is allowed to do.
Operations may already be processing something related to the same customer.
A human may need to approve one specific action.
Another system may update the record halfway through the process.
Now the clean chain becomes a network.
And networks behave differently.
Information can arrive out of order.
Different agents can hold different versions of the truth.
Tasks can be duplicated.
Requests can loop.
Dependencies can fail.
One agent can make a decision based on information that another agent has already superseded.
This is why I think agent communication has to be treated as architecture, not conversation.
The moment multiple agents are responsible for meaningful work, the system needs rules governing how information moves.
Agents should communicate because the workflow requires it
One mistake I think we will see repeatedly is giving agents the ability to communicate simply because we can.
It sounds advanced.
It usually looks impressive.
But more communication does not automatically create a better system.
In fact, unnecessary communication creates complexity very quickly.
If five agents all have access to the same information and can freely communicate with one another, it becomes harder to understand who is responsible for what.
Why did Agent A ask Agent C?
Why did Agent C ask Agent D?
Why did Agent D return something different from Agent B?
Which response should be trusted?
You have created intelligence.
You have also created ambiguity.
I prefer starting from responsibilities.
What is this agent actually responsible for?
What information does it require?
Which other roles does it genuinely need to interact with?
Under what circumstances?
That produces a much narrower communication graph.
A finance agent should not know everything simply because information exists elsewhere in the organisation.
It should receive what is necessary to complete the financial task.
A customer service agent does not need unrestricted access to every internal operational decision.
It needs the information necessary to serve the customer.
This sounds restrictive.
That is the point.
Enterprise systems become safer when communication is intentional.
I think agent communication will start looking a lot like organisational design
Traditional companies already have communication architecture.
We just don’t normally call it that.
Employees have reporting lines.
Departments have responsibilities.
Certain information stays inside certain teams.
Some requests go through managers.
Some decisions require approval.
Some departments interact constantly.
Others barely interact at all.
There are escalation paths.
There are handoffs.
There are meetings specifically designed to move information between parts of the organisation.
Some of those processes are inefficient.
But the underlying principle makes sense.
Information should move according to responsibility.
I think enterprise AI will eventually inherit a similar structure.
Not because agents need corporate bureaucracy.
Because responsibilities need boundaries.
Imagine an organisation with a customer service agent, billing agent, compliance agent and refund agent.
A customer asks why a payment was taken twice.
The customer service agent can identify the account and determine that the query relates to billing.
It asks the billing agent for transaction information.
The billing agent determines that a duplicate payment appears to have occurred.
But it cannot issue a refund.
That responsibility belongs to the refund agent.
The refund agent receives the relevant transaction evidence.
If the amount is below a defined threshold and all conditions are satisfied, it acts.
If the refund exceeds the threshold, it escalates to a human.
That is communication.
But it is structured communication.
Every participant has a role.
Every handoff has a reason.
Every boundary exists deliberately.
The orchestrator question matters
Once you have multiple agents, one of the first architectural decisions is who coordinates them.
One approach is central orchestration.
A primary agent, router or workflow layer receives the task and decides which specialist should handle each step.
That can make the system easier to understand.
There is a clear point of coordination.
The orchestrator knows where the work is going.
It can maintain workflow state.
It can enforce rules.
It can decide when the process is complete.
For many enterprise use cases, I think this will remain extremely useful.
But it also creates a dependency.
If everything relies on the orchestrator, the orchestration layer becomes critical infrastructure.
It can become a bottleneck.
Its mistakes affect the entire system.
The alternative is more decentralised communication.
Agents understand which other agents exist and communicate directly when necessary.
That can create more flexibility.
It can also become chaotic remarkably quickly.
Now you have agents independently deciding who should receive information.
You need discovery.
Routing logic.
Permission enforcement.
Loop prevention.
Conflict handling.
State management.
And a way to reconstruct what happened afterwards.
Neither model is universally correct.
I suspect the strongest enterprise architectures will often use a hybrid.
Central coordination for important workflows.
Direct communication for tightly controlled interactions.
The architecture should reflect the process rather than forcing every process into the same communication model.
Handoffs need contracts
I think this is one of the most important concepts in multi-agent architecture.
When one agent hands work to another, the receiving agent should not have to guess what it has been given.
There should be a contract.
Not necessarily a legal contract.
A technical one.
What information is being passed?
What format should it use?
What action is being requested?
What is the expected response?
What permissions are relevant?
What metadata needs to travel with the request?
What happens if required information is missing?
If agents simply communicate through free-form natural language, flexibility is high.
So is ambiguity.
Consider this message:
“Customer looks eligible for a refund, can you sort it?”
A human may understand what that means.
An enterprise system should probably be more precise.
Which customer?
Which transaction?
Which amount?
Why is the customer considered eligible?
What evidence supports that decision?
Has the refund policy been checked?
Is the request for assessment or execution?
Who initiated it?
What is the current workflow state?
Structured handoffs make systems easier to validate.
They also make them easier to audit.
This is why I think natural language will often sit on top of structured communication rather than replace it completely.
Humans can speak ambiguously.
Infrastructure should not.
Shared context sounds attractive until everything can see everything
Another early design choice is context.
If multiple agents are working together, should they share the same memory?
It sounds efficient.
Everyone knows what everyone else knows.
Nobody has to repeat anything.
But that creates problems.
The first is security.
Different agents may operate under different permission boundaries.
If they all share one unrestricted context, those boundaries start collapsing.
The second is relevance.
A finance agent does not necessarily benefit from receiving the entire conversation history of a customer service process.
More context is not always better context.
Large amounts of irrelevant information can make reasoning worse, increase cost and make behaviour less predictable.
The third is provenance.
If an agent knows something, where did it come from?
Did another agent infer it?
Was it retrieved from a system?
Was it supplied by a user?
Is it still current?
Shared memory can make these distinctions difficult.
I prefer thinking about context as something that should be transferred deliberately.
The receiving agent gets what it needs.
No more than necessary.
And ideally, the information carries enough provenance for the agent to understand what it is looking at.
That is much closer to how secure enterprise systems already handle information.
Context should have provenance
I think provenance will become a much bigger topic in enterprise AI.
An agent should not simply know something.
It should often know why it knows it.
Imagine a compliance agent receives the information:
“Customer has passed verification.”
Where did that statement come from?
Was it generated by another agent?
Was it returned by the identity provider?
Was it entered manually by an employee?
Was it verified yesterday or three years ago?
Those details can completely change the meaning.
This is where communication between agents needs metadata.
Source.
Timestamp.
Confidence.
Authority.
Version.
Workflow ID.
Perhaps even the exact underlying record.
That sounds less exciting than giving agents personalities and letting them talk to each other.
It is also the kind of thing that makes multi-agent systems trustworthy.
The deeper we go into enterprise AI, the more I think traceability will matter.
Not just what the system decided.
What information travelled through the system before that decision was made.
Agents should not be able to delegate authority they do not possess
This becomes critical when agents can ask other agents to act.
Imagine a customer service agent is not authorised to issue refunds.
That makes sense.
So it asks the refund agent.
The refund agent is authorised.
Problem solved?
Not necessarily.
If any agent can request anything from an authorised agent, then the original permission boundary is meaningless.
You have simply moved the authority sideways.
The refund agent needs to validate the request.
Who is asking?
Is that requester allowed to initiate this type of action?
Does the request satisfy the required conditions?
Is approval needed?
Does the refund agent have sufficient evidence?
The requesting agent should not be able to transfer authority it never had.
This is a familiar concept in security architecture.
It becomes extremely important in agent systems.
Otherwise, one compromised or badly behaving agent can use another agent’s permissions to perform actions outside its intended role.
The communication layer therefore becomes part of the permission architecture.
Messages are not just information.
Some messages are requests for authority.
That distinction matters.
There needs to be a difference between asking and instructing
Human organisations understand this instinctively.
If I ask another department for information, that is different from instructing them to perform an action.
AI systems need the same distinction.
An agent may query another agent.
It may request analysis.
It may propose an action.
It may instruct an action.
It may escalate an issue.
It may publish an event.
Those are different communication types.
Treating every interaction as generic agent-to-agent messaging throws away useful structure.
An instruction should probably require stronger validation than a question.
A financial action should require stronger controls than an information request.
An escalation should preserve the context that caused the escalation.
A published event may need to reach several subscribers without any expectation of a direct response.
This is where traditional software architecture becomes extremely relevant again.
Queues.
Events.
APIs.
Schemas.
Access controls.
Transactions.
Retries.
Idempotency.
Observability.
AI does not eliminate these ideas.
It makes them more important because the component initiating the action is less deterministic.
Event-driven systems will probably matter a lot
Not every agent interaction needs to be a conversation.
Sometimes something simply happens.
A customer payment fails.
A contract is uploaded.
An order changes status.
A risk threshold is crossed.
A new employee joins.
A support ticket becomes urgent.
Those events can trigger work.
Instead of one agent continuously asking other agents whether anything has changed, the infrastructure can publish the event.
Relevant agents respond.
The finance agent reacts to a payment failure.
The customer service agent prepares communication.
The risk agent evaluates whether the failure changes the customer profile.
Perhaps no central agent needs to manually coordinate any of it.
This is one reason I think event-driven architecture and AI agents fit together naturally.
The business already produces events.
Agents become intelligent participants responding to those events.
But again, the same questions appear.
Which agents are subscribed?
What information does the event contain?
What permissions apply?
What happens if an agent fails?
Can the event be processed twice?
How do you know every required action completed?
These are not new computer science problems.
AI is simply entering the systems where those problems already existed.
Idempotency is a boring word until an agent charges somebody twice
Some of the most important parts of AI architecture sound painfully uninteresting.
Idempotency is a good example.
Imagine an agent requests a payment.
The payment system processes it successfully.
But the confirmation response times out.
The agent thinks the request failed.
So it tries again.
Now the customer has been charged twice.
The AI may have behaved perfectly rationally based on the information available to it.
The architecture failed.
The same issue can appear with refunds.
Emails.
Record updates.
Orders.
Account changes.
Anything where repeating an action creates a different outcome.
Communication between agents and systems therefore needs to account for retry behaviour.
Was this action already completed?
Is this the same request?
Can it safely be attempted again?
This is why enterprise AI systems cannot rely on intelligence to compensate for weak infrastructure.
Good architecture assumes things will fail.
Then it makes failure safe.
Loops are another problem nobody notices in the demo
Agent A asks Agent B for clarification.
Agent B determines that Agent C needs to answer.
Agent C asks Agent A for additional context.
Agent A sees the request and sends it back to Agent B.
Congratulations.
You have built a meeting.
This sounds funny until autonomous systems are consuming compute, triggering tools or delaying real workflows.
Multi-agent communication needs loop detection.
Task identifiers.
Hop limits.
State awareness.
Rules for when a request has already been handled.
Escalation paths when agents cannot resolve something.
Without those controls, a flexible agent network can easily become an expensive way to move confusion around.
Again, the solution is not necessarily making the model smarter.
The solution is giving the system structure.
Agents are going to disagree
This is another area I think becomes fascinating.
If you have specialised agents analysing the same situation from different perspectives, disagreement is inevitable.
The commercial agent thinks the deal should proceed.
The risk agent thinks it should not.
The legal agent identifies a contractual issue.
The finance agent says the economics still work.
What happens?
Humans already deal with this inside organisations.
Different departments optimise for different outcomes.
The company needs decision rules.
Sometimes one role has authority.
Sometimes a committee decides.
Sometimes the issue is escalated.
Sometimes the decision depends on thresholds.
AI organisations will need the same thing.
You cannot solve disagreement by asking the agents to keep talking until they magically agree.
Sometimes they should not agree.
Their different objectives are exactly why they exist.
The architecture needs to determine how conflicting outputs become a decision.
That may involve another agent.
It may involve deterministic policy.
It may involve a human.
It may depend on the type of decision.
This is where multi-agent systems start resembling governance structures rather than chat rooms.
Not every decision belongs to another agent
There is a temptation when designing agentic systems to solve every problem with another agent.
Need coordination?
Add an orchestrator agent.
Need validation?
Add a validator agent.
Need safety?
Add a safety agent.
Need conflict resolution?
Add another agent.
Eventually you have twenty-seven agents discussing whether somebody can receive a £40 refund.
Some decisions are better handled deterministically.
If the policy says refunds under a defined amount can be processed when three specific conditions are true, software can enforce that.
No reasoning is required.
If a permission should never be granted, enforce it at the infrastructure layer.
If a workflow must always follow a particular sequence, encode it.
AI is useful where judgement, interpretation or ambiguity exists.
Deterministic software remains extremely good at deterministic problems.
The strongest enterprise AI systems will combine both.
Agents where intelligence is valuable.
Software where certainty is valuable.
Observability becomes the nervous system
Once multiple agents are communicating, you need to be able to see what is happening.
Not just whether the final answer was correct.
How did the system get there?
Which agent initiated the workflow?
Which agents became involved?
What messages were exchanged?
Which tools were called?
What information was retrieved?
How long did each step take?
Where did the process fail?
How much did it cost?
Did a human intervene?
Was a permission denied?
Did the workflow loop?
Without that visibility, debugging becomes guesswork.
And the more autonomous the system becomes, the more dangerous guesswork becomes.
This is why I think observability will become one of the most important layers in enterprise agent architecture.
Eventually, organisations will probably monitor agent systems in a similar way to how they monitor infrastructure today.
Failures.
Latency.
Throughput.
Escalation rates.
Tool usage.
Cost.
Confidence.
Policy violations.
Unexpected communication patterns.
Perhaps even behavioural drift.
If agents are becoming part of the operating system of the enterprise, companies will need an operational view of that system.
Communication creates cost too
There is also a purely economic reason to architect communication properly.
Agent communication is not free.
Every message can require inference.
Every transfer can require retrieval.
Every additional context window consumes tokens.
Every unnecessary reasoning step creates latency.
Multiply that across thousands or millions of workflows and poor communication design becomes expensive.
A system where five agents repeatedly summarise information for each other may technically work.
It may also be financially ridiculous.
This matters even more as enterprise AI moves from occasional assistance to continuous operation.
Cost needs to be designed into the architecture.
Can an event carry structured data instead of requiring another model call?
Can the receiving agent access the source directly?
Does the agent need the entire history?
Can deterministic routing replace reasoning?
Can a cheaper model handle certain interactions?
Should some communication be asynchronous?
Efficiency is not just about infrastructure cost.
It also improves reliability.
Every unnecessary step is another place something can go wrong.
The communication architecture should reflect the risk
Not every workflow deserves the same architecture.
An internal research agent summarising competitor information can tolerate more flexibility.
A healthcare agent modifying a patient record cannot.
A marketing agent drafting a social post may be allowed to communicate broadly.
A financial agent initiating a transaction should operate inside much tighter boundaries.
This is why I don’t think there will be one universal enterprise multi-agent architecture.
The communication model should reflect consequence.
Low-risk systems can optimise more heavily for speed and autonomy.
High-risk systems should optimise for control, traceability and predictable behaviour.
The more consequential the action, the less comfortable I become with unrestricted agent autonomy.
That does not mean the AI becomes less useful.
It means the intelligence is being deployed responsibly.
Human communication will become part of the same architecture
There is another part of this that I think gets overlooked.
Agents are not only going to communicate with agents.
They will communicate with people.
And human involvement should not sit outside the architecture.
Imagine an agent reaches a decision it cannot make.
It escalates to an employee.
What does the employee receive?
A vague message saying:
“Please review.”
Or a structured explanation containing the customer, the relevant information, what the agent has already checked, why it cannot continue and exactly what decision is required?
The second is obviously more useful.
Then what happens after the human responds?
Does the workflow resume automatically?
Does the agent understand the decision?
Is the response logged?
Can similar future decisions be handled differently?
Human handoffs need the same level of design as agent handoffs.
Otherwise, AI simply moves bottlenecks around.
A good system should make human intervention easier, not create another inbox for people to monitor.
I think agents will eventually have communication policies
Today, we often think about agent behaviour through prompts.
In future enterprise environments, I think communication itself will increasingly be governed by policy.
This agent can communicate with these agents.
It can request these types of information.
It can publish these events.
It cannot initiate these actions.
Messages containing certain data cannot leave this environment.
Requests above this threshold require approval.
This workflow cannot proceed unless these conditions are satisfied.
Communication with this external system must be logged.
This agent can receive information from finance but cannot send customer data back.
That starts looking less like prompt engineering and more like access control.
Which is exactly what it should look like.
Because once agents can act across an organisation, communication is part of security.
The enterprise agent network will probably become its own layer
This is where I think things eventually get really interesting.
Today, most companies have systems.
CRM.
ERP.
HR systems.
Finance systems.
Databases.
Communication platforms.
Data warehouses.
Then employees sit above those systems and move information between them.
AI agents begin changing that model.
The agents themselves become an operational layer.
They receive events.
Interpret information.
Coordinate work.
Use tools.
Ask other agents for specialist input.
Escalate to humans.
Update systems.
Monitor outcomes.
At enough scale, that starts looking like a network sitting across the enterprise.
Not replacing the underlying software.
Connecting intelligence across it.
This is one of the reasons I keep coming back to the idea of AI sub-agent divisions.
The agent is useful.
The division is more interesting.
Because once specialised agents operate together, communication becomes the mechanism that turns separate capabilities into an organisation.
I think the organisational chart eventually gets a communication map
We are used to looking at organisations through hierarchy.
CEO.
Leadership.
Departments.
Teams.
Employees.
But hierarchy only tells you who reports to whom.
It does not necessarily show how work moves.
AI may force companies to model that second layer more explicitly.
Which agents communicate?
Which systems do they access?
Which events trigger them?
Which decisions can they make?
Where does information cross departmental boundaries?
Where do humans intervene?
Where does authority change?
Where does data stop?
That map may eventually become just as important as the organisational chart itself.
Because when digital workers are operating continuously across the company, information flow becomes part of organisational structure.
And perhaps this is one of the larger changes enterprise AI creates.
We are not simply adding intelligence to existing software.
We are beginning to formalise how work, authority and information move through the company.
The goal is not to make agents talk more
I think this is the part worth remembering.
Multi-agent systems are often demonstrated through conversation because conversation is easy for us to understand.
One agent speaks.
Another responds.
Something intelligent appears to happen.
But enterprise communication cannot be judged by how impressive the conversation looks.
The best system may involve remarkably little conversation.
A clean event.
A structured handoff.
A validated request.
A deterministic permission check.
One specialist agent making one judgement.
A human approving the exceptional case.
Then the workflow continues.
That is much less theatrical.
It is also much closer to infrastructure.
And that, for me, is where enterprise AI becomes interesting.
The future will not be defined by how many agents we can make talk to each other.
It will be defined by whether we can give them the right information, at the right time, under the right authority, without losing control of the organisation in the process.
Building intelligent agents is getting easier.
Designing how intelligence moves between them is going to be much harder.
And I suspect that communication layer will eventually become one of the most important pieces of enterprise AI architecture.
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