Walk through any large company today and you will find an astonishing array of software. Hundreds of SaaS applications, dozens of separate automation tools, and now a fast-growing crowd of AI agents stacked on top of all of it. By every measure, businesses are more connected than they’ve ever been. Yet the people who actually use the tools, employees and customers alike, keep getting lost in the blind alleys between them.
Data gets moved around, but the work itself is plagued with stops and starts. Customers get tired of explaining the same problem three times to three different channels, and each one acts as if the conversation is starting fresh. AI pilots dazzle in the demo, but then fizzle out before they make it into daily operations. Meanwhile the IT team spends most of its week keeping fragile one-to-one connections alive instead of building anything new.
But these aren’t new issues, they’re part of a sixty-year story of moving files between two systems and trying to coordinate entire business processes across people, software, and now AI agents, all in search of a unified experience.
What Is a Unified Experience?
A unified experience describes what a customer or employee has when integration, automation, APIs, data, and AI agents answer to one coordinating layer instead of working as islands. The seams disappear and what reaches a user feels like a single interaction even when dozens of tools operate out of sight.
Making independent systems behave as one has occupied companies for sixty years, and each decade added a verb: integrate, automate, orchestrate. To understand what is process orchestration, why it has become the center of the enterprise stack, and why AI makes it imperative, it helps to start at the beginning.
The Origins: From Point-to-Point Integration to the First Hubs
The 1960s
The first real attempt at enterprise integration barely counted as software. In the 1960s, the U.S. transportation industry agreed on shared formats for trading paperwork so that invoices, manifests, and purchase orders could pass between partners electronically instead of being retyped by hand at each stop.
Much of the credit belongs to Edward Guilbert, who had standardized logistics documents for the 1948 Berlin Airlift and brought the same rigor to the railroads, trucking and shipping. The practice, electronic data interchange (EDI), predates nearly all the enterprise software still in service, and later versions of it still handle healthcare claims and cross-border trade.
Inside companies, the early approach to connecting two applications was simple and direct: write a small script that pushes data out of one system and into another. That worked fine when you only had a handful of systems. The trouble was that the number of links you needed climbed far faster than the number of systems you had, because in the worst case every system may need to talk to every other one.
The math behind it was the handshake formula n(n-1)2, and it got messy quickly. A company with a few applications could find itself maintaining dozens of separate connections, each with its own format, its own error quirks, and its own owner. The whole web quickly became unstable and expensive to keep alive.
The 1970s
Near the end of the 1970s, a small Unix utility called cron shipped with Version 7 Unix and, for the first time, engineers had a standard, dependable way to tell a machine “run this job at this time.”
It sounds modest, but cron is the conceptual ancestor of every workflow and scheduling tool that came after it. Almost everything we now call automation traces back to that one idea of triggering work on a defined schedule.
The 1990s
The 1990s made the connection problem much worse as companies bought packaged systems for nearly every function: enterprise resource planning (ERP), human resources, finance, customer relationship management, and more.
More systems meant more silos and more integration work. The industry’s answer was Enterprise Application Integration (EAI) middleware, built mostly on a hub-and-spoke design, each application connecting through an adapter to a central hub that translated formats and routed messages.
The hub model brought three genuinely useful ideas:
- A shared messaging layer
- A common canonical data model
- Central transformation
But it also became a bottleneck and a single point of failure, since everything ran through it.
The canonical model turned into a never-ending committee exercise, because every change had to be negotiated across the whole organization.
And, most tellingly, the rules for what should happen next in a business process had no proper home of their own.
EAI moved data reliably, but the logic of the process, the order of the steps, and the surrounding conditions stayed buried inside individual applications or scattered across the connections between them.
Dependable plumbing, it turned out, was not the same as coordinating what the plumbing was for, and the architecture that soon emerged to provide that would also give this entire discipline its name.
When “Orchestration” Got Its Name: SOA, ESBs, and BPM
The late 1990s to 2000s
The thinking changed around the turn of the century. Rather than treating each application as one big sealed block, the new approach was to expose an application’s functions as reusable services that any other system could call. That was Service-Oriented Architecture (SOA).
To carry messages between those services, vendors built the Enterprise Service Bus (ESB), a lighter, standards-based backbone that replaced the heavy EAI hub. This was not just a faster version of the old idea, it was a genuinely different architecture, built around reusable, recombinable services instead of fixed point-to-point pipes.
The word “orchestration” entered the vocabulary here, and the musical image was deliberate. An orchestra needs someone to decide who plays which part, when each instrument comes in, and how loudly it plays. An ESB could do something similar with software: call several services in the right sequence to satisfy a single business request. For a while, that level of coordination was as good as it got, until another form of orchestration emerged.
What Is Process Orchestration?
Process orchestration decides what happens with the integration data, in what order, by which actor, and how to recover when a step breaks. It’s the coordination of complete, end-to-end work: pulling together the applications that take part, the employees whose decisions move it forward, the information all of them depend on, and, increasingly, the AI agents now joining in, all governed from one central layer rather than stitched together through a tangle of point-to-point connections.
A complementary discipline covered the human side: Business Process Management (BPM). BPM platforms sat on top of the messaging layer and added the things software-to-software calls could not cover on their own, such as approvals, escalations, service-level agreements, and the messy exceptions that never fit a clean script. A follow-up modeling standard called BPMN 2.0 was released in 2011 and gave business analysts and developers a shared, visual notation for describing how a process should run, so the people defining a process and the people building it could finally work from the same picture. The approach proved its worth in heavily regulated work like insurance claims, lending, and credit decisions.
Despite the gains in open standards, reusable services, machine-readable contracts, and real process governance, the era was costly all the same: ESB programs needed specialist teams, long timelines, and significant up-front spending. As cloud computing and SaaS began to take over in the following decade, the on-premises ESB increasingly looked like the wrong tool for a world that was rapidly moving off premises. The architecture that had solved one era’s problem was about to become the next era’s bottleneck.
The Cloud Era: iPaaS, APIs, RPA, and the Rise of Tool Sprawl
The 2010s
Three changes reshaped enterprise software in the 2010s:
- SaaS became the default, so the number of applications exploded
- REST APIs became the standard way systems talked to each other
- The cloud emerged as the standard environment for integrations
But the on-premises ESB simply was not designed to handle any of this and to complicate matters even further, four kinds of tooling established themselves.
It’s helpful to keep them straight, because conflating them is a big part of why enterprise stacks ended up so fragmented:
- Integration platforms as a service (iPaaS): iPaaS moved managed middleware to the cloud and stretched it across on-premises and SaaS applications. Boomi began life in this space, cutting integration timelines that used to run for months on traditional EAI projects down to just weeks.
- API gateways and API management: These are the control plane for synchronous, request-and-response interactions. They give an organization a single place to publish, secure, throttle, and monitor the interfaces that other companies and internal teams consume.
- Robotic Process Automation (RPA): Software robots mimic a person clicking through screens, automating repetitive, rule-based desktop tasks at the user-interface level. RPA’s big appeal was that business teams could often deploy it without waiting for IT to rebuild back-end systems.
- Data pipeline orchestration: Led by Apache Airflow, this applied directed-graph scheduling to dependent extract, transform, and load (ETL) jobs in place of fragile cron scripts. It’s important to note that data pipeline orchestration is a different concept compared to business process orchestration. The word “orchestration” is doing double duty, and conflating the two regularly confuses people.
Orchestration and Automation
However, there can be an even deeper mix-up between orchestration and automation. Let’s set the record straight:
- Automation performs an action: it sends the message, writes the record, runs the script, returning the same output for the same input. Scheduled jobs, iPaaS recipes, and RPA bots all belong to this layer.
- Orchestration, by contrast, governs the action: it decides what to do, in what sequence, by which person or agent, and how to respond when something fails, holding the state of a process across its whole life.
But the relationship between the two is cooperative, not competitive, with automation running the individual parts, while orchestration runs the show.
If we take a look at a typical support case, we can better understand the dynamic:
- The automation side can handle the mechanical steps:
- Log the case
- Drop it in the right queue
- Fire off a “we got your request” note
- The orchestration side analyzes the context:
- How valuable is this customer?
- How urgent is the problem?
- Who is free to help?
- What kind of help does the situation actually call for?
Based on that assessment, the orchestration platform might hand a simple fix to a bot, push a tricky one to a specialist, or flag an at-risk account straight to a retention team.
Most enterprises need both orchestration and automation, plus iPaaS to move the data and API management to control the synchronous calls.
The 2020s
By the end of the 2010s, a typical large enterprise was running iPaaS for SaaS integration, API gateways for external exposure, dozens of RPA bots for desktop automation, and Airflow or something like it for data pipelines, with no single layer coordinating any of it.
Almost no company plans its way into that tangle, the complexity arrives gradually, through a long string of choices that each made sense at the time: a quick script, a niche software purchase, a stopgap that quietly becomes permanent.
None of these is a bad call on its own, but after a few years, the business is leaning on code no one documented with components scattered across servers nobody fully tracks, and keeping versions and patches in line becomes the work that takes over the IT team’s week.
The lesson is that the moment an outcome depends on several systems moving in step, fragmented automation runs out of road, and employees are left to close the gaps by hand.
Why AI Is Redefining What Is Process Orchestration
The 2020s
In the AI era, businesses now demand one hub where everything gets coordinated. Gartner estimates business orchestration and automation technologies (BOAT) grew by about 35% in 2025 to reach just under $7bn, and will surge to $21bn by 2029. This shows that the work that used to be scattered, with bots operating here, people deciding there, AI agents and the services they touch off running elsewhere, is increasingly being replaced by a single coordinating layer that reacts as things happen rather than on a fixed timetable. The key to achieving this is to stop treating orchestration as a back-office concern and make it the strategic center of the stack.
Why AI Breaks Traditional Integration
AI agents make a genuinely new argument for orchestration because two of their properties break the assumptions older integration was built on:
- Agents are non-deterministic: Traditional integration assumes an identical output for an identical input, but an AI model is non-deterministic and can return different results for the same prompt, removing the very foundation point-to-point integration relied on.
- Agents act: Rather than simply providing an answer, agents sense their environment, make decisions, call APIs, start workflows, and edit records.
Anything that takes real action like this on company systems has to be grounded in governance, observability, and human-in-the-loop oversight, which is precisely an orchestration layer’s job. Skip it, and each team stands up its own agents, on whatever platform it favors, wired to its own copy of the data, recreating the very fragmentation integration was supposed to cure.
The Adoption Gap
McKinsey’s 2025 survey found that 62% of organizations are experimenting with AI agents, but only 23% are actually scaling them, and within any single business function no more than 10% report agents running in large numbers. The main bottleneck is an orchestration gap, not model quality, and it won’t close by itself. Neither can businesses afford to avoid dealing with it and risk getting left behind.
Gartner expects 40% of enterprise applications to embed task-specific agents by the end of 2026, up from under 5% in 2025, while the agent market itself is projected to grow from roughly $7.6bn in 2025 to reach up to $52bn by 2030.
As those agents start acting on real systems, their every action has to be auditable, so policy, audit trails, and oversight must be imposed by the coordination layer around them. Companies are already budgeting for this with spending on AI governance forecast to total $492m by the end of 2026 and to rise to $1bn by 2030.
The Authentication Problem
One particular governance problem deserves singling out, because many CIOs have not yet considered it holistically. When an AI agent calls a business system on a user’s behalf, how does it authenticate?
The easiest answer is to hand the agent one global “superuser” service credential with broad access to the target system, but that’s dangerous. For instance, give an HR agent that reach, and when a plumber asks for the chief executive’s salary, the agent can technically retrieve and reveal it. If you’re hoping your written instructions are good enough to stop the model from oversharing, that’s not a realistic expectation of control.
The right approach is to implement delegated authorization. Instead of a shared credential, the agent makes each call using the end user’s own identity and permissions, typically through OAuth2, so the workforce system and the company’s single sign-on decide what that person may see, and the model never has to judge what is sensitive. This is a platform-level problem, and it is best solved at the orchestration layer rather than reinvented agent by agent.
The key takeaway to remember is that, without orchestration, AI agents are a liability, but the same agents inside a well-governed orchestration layer providing process control, governance, output validation, and human checkpoints are a force multiplier.
2026: Unified and Governed Agentic Workflow Orchestration
In 2026, the pilots and proofs-of-concept that dominated the last two years are giving way to unified platforms purpose-built to orchestrate agents alongside everything else the business already runs.
Boomi Orchestrate
Boomi Orchestrate offers a single place where APIs, integrations, and agents are wired together and directed through a natural-language interface. It is designed so a business user can simply talk through a process, describe the outcome they want, and watch the platform stitch the pieces into a working flow. The historic divide, where business teams write requirements and IT translates them into integrations weeks later, disappears almost completely when the request and the build can happen in the same conversation.
However, that’s only possible when the agents driving the flow have a safe, controlled way into the systems where the real work happens.
Boomi MCP
The Model Context Protocol (MCP) has become the standard way to open up enterprise systems to agents, giving any model a consistent method for discovering what tools exist and how to use them. Without a shared protocol, every agent platform would have to negotiate its own connection to every system, which is precisely the point-to-point trap the industry has spent six decades trying to escape.
Boomi MCP collects every server in one vendor-agnostic catalog, drawing on Boomi itself, third-party registries, and the Official MCP Registry, with audit trails and lifecycle controls plugged in for the full timeline from a server’s first publication to its eventual retirement. The vendor-agnostic element prevents tying the catalog to one model provider, avoiding the lock-in the broader MCP standard was meant to prevent.
But how can you actually expose your tools to agents without rebuilding authentication one connector at a time?
Boomi Connect
Boomi Connect turns the catalog into something IT can govern in practice, surfacing more than a thousand pre-built, managed connectors while OAuth flows, token refresh, and per-user identity are handled centrally rather than wired one by one. The same governed access shows up inside the AI tools employees are already using, including Claude, Copilot, and Gemini, so IT keeps a single control plane while end users retain the interface they already prefer.
Orchestrate, MCP, and Connect manage how agents behave once they are running. So, how do the integrations and workflows underneath them get created in the first place?
Boomi Companion
Boomi Companion is a set of open-source agent skills that drop into the coding environments developers already run, such as Claude Code, OpenAI Codex, and Google Antigravity.
The skills cover integration processes, EDI, and event streams, which is most of what a developer would otherwise be assembling by hand. You can describe the integration in plain language, and the skill takes over, building components, deploying to a runtime, running tests, reading the logs, and iterating until the result holds up.
In one internal assessment, an EDI specialist who normally needed about an hour and 20 minutes to hand-build a single profile and map watched Boomi Companion produce four of them in only 20 minutes.
How Boomi Delivers Process Orchestration for Unified Experiences
That long sixty-year quest to move from connected systems to coordinated ones that deliver a seamless unified experience is precisely the challenge Boomi was built to overcome.
Rather than buying a separate product for each layer and then straining to make them cooperate, Boomi fuses integration, automation, API management, data management, and AI agent orchestration together in one platform.
Named a Leader in the 2026 Gartner® Magic Quadrant™ for iPaaS and a Leader in the 2025 Forrester Wave™ for iPaaS, Boomi helps organizations integrate everything, automate anything, and orchestrate frictionless processes, bringing order to the digital chaos of modern business.
In addition to Boomi Orchestrate, Boomi MCP, Boomi Connect, and Boomi Companion, the Boomi Enterprise Platform supplies you with an AI-ready foundation, thanks to:
- Boomi Flow delivers low-code process orchestration and workflow automation. With advanced error handling, when a bot hits an anomaly, Flow hands the case to the right person and keeps it moving.
- Boomi Data Hub provides master data management, the trustworthy single source that orchestrated processes and AI depend on.
- Boomi API Control Plane governs and secures every API and data interaction, including the ones AI agents drive, tracking data movement across regions for rules such as GDPR and CCPA.
- Boomi Event Streams brings event-driven architecture, the real-time, scalable communication that agents and workflows need to react the moment something happens.
- Boomi Agentstudio and the platform’s built-in agents let teams build, orchestrate, and govern AI agents in the same environment that runs the integrations and processes.
- Boomi GPT is a conversational front end that lets people coordinate multiple agents in plain language.
- Boomi DesignGen automatically designs integration processes and data mappings, drawing on a library Boomi cites at over 300 million patterns.
- Boomi Pathfinder suggests the next best step and recommends ways to optimize workflows.
- Boomi Scribe generates documentation automatically, supporting transparency and compliance.
- Boomi DataDetective classifies personally identifiable information (PII) and tracks data movement for data-protection compliance.
- Boomi Answers gives quick, accurate answers about the platform, drawing on a community knowledge base of more than 240,000 members.
Ready to see how Boomi helps you move from connected systems to coordinated, AI-ready operations? Explore Boomi Orchestrate and discover what a truly unified experience looks like in your business.