Fintech Brex is betting that the future of enterprise AI isn’t better orchestration — it’s less of it.
As generative AI agents move from copilots to autonomous systems, Brex CTO James Reggio says traditional agent orchestration frameworks are becoming a constraint rather than an enabler. Instead of relying on a central coordinator or rigid workflows, Brex has built what it calls an “Agent Mesh”: a network of narrow, role-specific agents that communicate in plain language and operate independently — but with full visibility.
“Our goal is to use AI to make Brex effectively disappear,” Reggio told VentureBeat. “We’re aiming for total automation.”
Brex learned that for its purposes, agents need to work in narrow, specific roles to be more modular, flexible, and auditable.
Reggio said the architectural goal is to enable every manager in an enterprise “to have a single point of contact within Brex that’s handling the totality of their responsibilities, be it spend management, requesting travel, or approving spend limit requests.”
The journey from Brex Assistant
The financial services industry has long embraced AI and machine learning to handle the massive amounts of data it processes. But when it comes to bringing AI models and agents, the industry took a more cautious road at the beginning. Now, more financial services companies, including Brex, have launched AI-powered platforms and several agentic workflows.
Brex’s first foray into generative AI was with its Brex Assistant, released in 2023, which helped customers automate certain finance and expense tasks. It provides suggestions to complete expenses, automatically fills in information, and follows up on expenses that violate policies.
Reggio acknowledges that Brex Assistant works, but it’s not enough. “I think to some degree, it remains a bit of a technology where we don’t entirely know the limits of it,” he said. “There’s quite a large number of patterns that need to exist around it that are kind of being developed by the industry as the technology matures and as more companies build with it.”
Brex Assistant uses multiple models, including Anthropic’s Claude and custom Brex-models, as well as OpenAI’s API. The assistant automates some tasks but is still limited in how low-touch it can be.
Reggio said Brex Assistant still plays a big role in the company’s autonomy journey, mainly because its Agent Mesh product flows into the application.
Agent Mesh to replace orchestration
The consensus in the industry is that multi-agent ecosystems, in which agents communicate to accomplish tasks, require an orchestration framework to guide them.
Reggio, on the other hand, has a different take. “Deterministic orchestration infrastructure … was a solution for the problems that we saw two years ago, which was that agents, just like the models, hallucinate a lot,” Reggio said. “They’re not very good with multiple tools, so you need to give them these degrees of freedom, but in a more structured, rigid system. But as the models get better, I think it’s starting to hold back the range of possibilities that are expanding.”
More traditional agent orchestration architectures either focus on a single agent that does everything or, more commonly, coordinator/orchestrator plus tool agents that explicitly define workflows. Reggio said both frameworks are too rigid and solve issues more commonly seen in traditional software than in AI.
The difference, Reggio argues, is structural:
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Traditional orchestration: predefined workflows, central coordinator, deterministic paths
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Agent Mesh: event-driven, role-specialized agents, message-based coordination
Agent Mesh relies on stitching together networks of many small agents, each specializing in a single task. The agents, once again using the hybrid mix of models as with the Brex Assistant, communicate with other agents “in plain English” over a shared message stream. A routing model quickly determines which tools to invoke, he said.
A single reimbursement request triggers several tasks: a compliance check to align with expense policies, budget validation, receipt matching, and then payment initiation. While an agent can certainly be coded to do all of that, this method is “brittle and error-prone,” and it responds to new information shared through a message stream anyway.
Reggio said the idea is to disambiguate all of those separate tasks and assign them to smaller agents instead. He likened the architecture to a Wi-Fi mesh, where no single node controls the system — reliability emerges from many small, overlapping contributors.
“We basically found a really good fit with the idea of embodying specific roles as agents on top of the best platform to manage specific responsibilities, much like how you might delegate accounts payable to one team versus expense management to another team,” Reggio said.
Brex defines three core ideas in the Agent Mesh architecture:
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Config, where definitions of the agent, model, tools and subscription live
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MessageStream, a log of every message, tool call and state transition
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Clock, which ensures deterministic ordering
Brex also built evaluations into the system, in which the LLM acts as a judge, and an audit agent reviews each agent’s decisions to ensure they adhere to accuracy and behavioral policies.
Success so far
Brex says it has seen substantial efficiency gains among its customers in its AI ecosystem. Brex did not provide third-party benchmarks or customer-specific data to validate those gains.
But Reggio said enterprise customers using Brex Assistant and the company’s machine learning systems “are able to achieve 99% automation, especially for customers that really leaned into AI.”
This is a marked improvement from the 60 to 70% Brex customers who were able to automate their expense processes before the launch of Brex Assistant.
The company is still early in its autonomy journey, Reggio said. But if the Agent Mesh approach works, the most successful outcome may be invisible: employees no longer thinking about expenses at all.
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