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Orchestrating AI Agents in Enterprise Workflows: Patterns for Reliability and Scale

AI is making it easier for companies to work on routine activities. Today, AI can do anything from filtering documents to fielding prospect questions. But, the spread of multiple AI agents in multiple work areas raises challenging management issues:

This is where the orchestration of AI agents starts to matter.

Orchestration rather than separate, autonomous AI agents. Instead of having separate AI agents working in isolation, orchestration links these individual processes into a specific order. The individual agents are responsible for a specific task while the overarching system ensures that they are guided appropriately.

What Are AI Agents?

An AI agent is a computer program capable of acquiring, reasoning about information and acting towards achieving a goal.

For instance, an example enterprise would have one agent to interpret the documents, one to verify the data, and one to input business information into a system. The agents could access CRM ERP databases, and other internal applications to perform the intended tasks.

What is fascinating about these agents is that, it is the interaction between them, which leads to the actual value.

Adding more agents alone will not lead to a better system. A business without tools to coordinate the agents' work can end up with duplicated processes, inconsistent data, broken processes, and hard to find errors.

That's why, this is where orchestration becomes important.

How AI Agent Orchestration Works

Orchestration can be thought of as the coach, who is the manager of a sports team.

The assignment of one agent for a task, and another to process, and another to check the result is the work of the orchestration layer. This layer figures out what should happen at any given time.

This can be organized in various ways.

Central Orchestrator

A central orchestrator controls the workflow and assigns tasks to different agents. This approach can make the process easier to understand, monitor, and audit.

It works well when the workflow has clear steps and dependencies.

Agent Choreography

In a choreographed system, agents respond to events rather than receiving instructions from one central controller. For example, when one agent completes a task, it can publish an event that triggers another agent.

This approach can support highly scalable workflows, although tracking the complete process can become more difficult.

Hierarchical Agents

A main agent can also manage several specialized agents. The main agent handles the larger objective while smaller agents focus on individual tasks.

This can be useful when a workflow contains several specialized operations.

Reliability Should Come First

Enterprise systems cannot rely on the AI always functioning flawlessly. External components may have failure, APIs may become inaccessible, individual agents may have faulty results.

An orchestration system that wants to be reliable should because of this be attack-proof.

Retry handling is another approach, where you may repeatedly attempt to complete a task if a transient service level problem occurs (and resume work if possible) instead of halting execution altogether.

Another is idempotency, which means that if a command is duplicated, even though we've executed it twice, this should not change the outcome. For example, this system should not double register the submission.

There are various means of doing this [supporting called conditions]. For example, businesses might have a circuit breaker, which can not call a service if it is already having problems. This stops an individual problem cascading through the whole process.

If you have a task, which cannot be processed using the automation system then you have an option of marking it for human editing.

Observability Makes AI Workflows Easier to Manage

Under multiple AI agents teaming, to fail is not enough they need to know where and why it failed.

Here is where observability comes into play.

Correlation IDs allow a service to follow a task from one agent to another or through various systems. Flows can then be obtained from logs, metrics, and traces:

Useful metrics include:

Task success rate. The percentages of correct key presses for key combinations in the 10 tested.

The time to process end-to-end.

Error rate: the overall false-negative or false-positive rate. This is the proportion of false allocations present in the total number of allocations in the cells of the error matrix.

The onset of action appears to be quick. Antipsychotic effects tend not to be seen by patients until at least 24 hours; some medications may take up to 7 days to have beneficial effects. It is reported that action occurs: "almost immediately." Though, the time taken is prolonged by side effects (e.g. sedation) that appear to mask the benefits. Once these effects are suppressed, studies show relief of symptoms is remarkably rapid.

Number of tasks processed. This metric represents how many tasks or jobs your system has processed. It is important as a server you like to see a high number of processed tasks.

Human intervention rate...

These measurements are used by engineering teams to detect bottlenecks and optimize the flow as time passes.

Scaling AI Agent Workflows

A workflow that handles 100 tasks may not do it in the same way for 100,000 tasks.

In an enterprise setting the agents should be designed in such a way so they can scale autonomously. A stateless agent with a strong centralised state store would enable such isolation.

Event-driven architecture is also a good one to use. Rather than having everything directly connected, you can also have services talk to each other over events. This means each section of the workflow can also be scaled individually.

Data contracts are significant too. Once the agents exchange information, the format of that information should be agreed upon by the majority. The value of versioning these contracts is that patching one system should not break any others.

It should go without saying that security must be considered from the start. Access control, data security and regulation rules should be embedded in the architecture and are not considered as an after-thought.

A Simple Example: Invoice Processing

Suppose we have a company where hundreds of invoices a month (or even many more) are arriving.

Rather than having employees read invoices one-by-one, type in all the details, verify the data and then update the ERP system, many AI agents could share the workload.

An OCR agent can read the invoice. A data extraction agent may identify fields such as the supplier, amount, and invoice number. A validation agent may validate the information, with a further agent handling the ERP posting.

A compliance agent can flag any transaction that appears to be suspicious and forward it to a human for processing. Lastly the notification agent can then inform a relevant team of the outcome.

Having this type of procedure also makes it less redundant.

Start Small and Grow Gradually

Trying to automate everything at once is one of the biggest mistakes a business can make.

A more effective way is to start with a single well-defined workflow. Define the input output, the business rules, and the expected outcome. Build a small MVP that only includes the needed agents for that process.

Add the extra agents and use cases if and when the workflow is operationally sound.

On top of that, this gradual manner simplifies the testing of security, the performance evaluation and the bugs detection, before the system reaches a higher complexity.

Final Thoughts

AI agents may enable automation of enterprise workflows. The difficulty is coordinating multiple agents.

Decomposing a business problem into a set of orchestration points based on AI agents is not enough. There should be a clear flow of the business process, robust data contracts, great observability, and well-stabilized security controls, and failure-handling mechanisms.

It's best to start with a narrow business process that has quantifiable results. Once it is working well, you can extend the architecture to more processes, systems, and AI agents.

When organizations move into AI automation, establishing an effective orchestration base can transition something from a savvy demo to a scalable enterprise.