Most large enterprises did not choose a fragmented automation stack on purpose. They bought a robotic process automation tool for the back office, a low-code platform for citizen developers, an integration layer to connect the two, a document processing tool for the paperwork neither could handle, and a case management system to hold it together when a process ran long. Each purchase solved a real problem at the time. The result, years later, is that 82% of organizations now say they fear "digital chaos" from the complexity this has created, and the average business manages roughly 50 separate endpoints just to execute a single process.¹
Then AI agents arrived, and most enterprises added them to the same fragmented stack. That has not gone well. An estimated $30–40 billion has gone into generative AI, and 95% of enterprises have no measurable financial return to show for it.² In PwC's most recent global CEO survey, 56% of chief executives report no financial return from AI at all, – not in cost, not in revenue.³
AI agents have changed what "orchestration" means
An automation tool used to be able to stay in its own lane. A bot ran its script, an integration moved data between two systems, a workflow routed a task and closed it out. An AI agent doesn't stay in a lane. It reasons over context, calls tools, hands work to other agents, escalates to a person when it hits something it shouldn't decide alone, and leaves a trail that has to be reconstructable across every system it touches. The moment agents start acting inside real business processes, the platform underneath them stops being a convenience and becomes the thing that determines whether the work is safe to run at all.
That is why bolting agents onto a fragmented stack tends to fail quietly. The agent works in the demo. It stalls on the way to production, because no single layer can enforce the rules, hold the process state, or produce the audit trail once the work crosses system boundaries. Security and risk concerns are now the top obstacle enterprises cite when they try to scale agentic AI,⁴ and cost is close behind: One in five organizations already limit their AI use because operating costs are too unpredictable to plan around, a pattern that holds across company sizes and industries.⁵ Neither of those is a reason to avoid agents. Both are reasons not to run them on infrastructure that was never built to govern them.
What a BOAT platform has to do
The category built to solve is business orchestration and automation technologies, or BOAT, and Gartner® defines it as a consolidated software platform that orchestrates and automates disparate business processes and tasks with varying degrees of autonomy and complexity across enterprise systems.⁷ The point of the category, in our understanding of the Gartner insights, is to bring traditional deterministic automation together with the newer probabilistic, agentic kind on one platform, rather than run them as separate stacks.
What separates a BOAT platform from the tools it replaces is what it has to do natively. Gartner states that a BOAT platform must provide native capabilities for AI agents' orchestration and governance, and multiagent coordination, support long-running multi-step processes, and connect through methods like Model Context Protocol, APIs, and direct UI interaction so agents can act across systems securely end to end.⁷ In our words, it consolidates what enterprises used to buy as separate BPA, RPA, integration, low-code, and document-processing products, and then orchestrates AI agents alongside the people, bots, APIs, and systems already running the work. The value of the single consolidated platform being the governance and cost control that only actually work when they're applied at the one layer where all the work runs, with a single audit trail across it, rather than bolted onto six tools separately and hoped to line up.
We feel the Gartner required-capabilities list sets the floor. In practice, five things separate the platforms that can actually govern agentic work from the ones that only claim to, regardless of which vendor you're evaluating:
- Policy enforcement. Business rules and compliance requirements have to be enforced deterministically, not left to a model's judgment on a given run.
- Scoped context and data. Agents should get only the data required for the step they're executing, not open-ended access to everything.
- Bounded agents. A narrowly scoped agent uses fewer tokens and has fewer ways to go wrong than one asked to reason across an entire process.
- Defined handoffs between people and AI. The system should escalate to a person automatically once a case crosses a risk threshold, rather than leaving that judgment to the agent.
- A traceable record of every decision. For any outcome, the platform should be able to show what data was available, which rule fired, and why the case was routed the way it was.
How Pega approaches it: Predictable outcomes, reimagined processes, predictable costs
Predictable outcomes for mission-critical work
Pega runs workflows, case management, real-time decisioning, and AI on a single rules-driven runtime, so there's no cross-engine synchronization to get wrong and governance stays consistent across every kind of automation. Case instances persist in Pega's own relational database across their full lifecycle with no enforced timeout, which is what lets a claim, an application, or an investigation that takes months to resolve stay intact and auditable instead of losing its state at a system boundary. A real-time decision engine sits inside the case lifecycle itself, adapting how work is routed based on predictive models and business rules without depending on a large language model to do that reasoning at runtime. These are the capabilities that make Pega a fit for regulated, mission-critical work, backed by certifications including FedRAMP High, HIPAA, and PCI DSS.
Reimagining processes for the AI era
AI can only scale as far as the process underneath it allows, and most enterprise processes are still built on decades-old systems. The cost of leaving them that way is real: the average global enterprise loses more than $370 million a year to the toll of failing to modernize legacy technology.⁶ Pointing a model at a broken process just adds a faster front end to the same bottleneck. Pega GenAI Blueprint takes natural-language requirements and existing legacy assets and generates executable process and application designs from them, so teams can accelerate discovery and design and cut the manual effort of modernizing complex processes. The point isn't to automate the old process faster. It's to reimagine the process for the AI era first, then automate it.
Predictable costs
Most AI vendors bill by the token, and token consumption is inherently variable: Iit moves with the complexity of the task, the length of the interaction, and how long the model "thinks." A cost structure that changes every time a model reasons harder is not something a business case can be built on. Pega prices agentic automation by process rather than by token, with no per-token meter. Because the approach is deterministic-first, much of the AI work happens at design time rather than at runtime, which reduces exposure to unpredictable token consumption and lets teams forecast automation costs as they scale.
What this means for a platform decision
If your automation stack has grown into six or more disconnected tools and you're now adding agents on top of it, the fragmentation problem doesn't get solved by adopting a seventh tool. It gets solved by consolidating onto a platform that can enforce rules, scope context, bound agents, define human handoffs, and trace every decision across the whole stack at once. Use the five criteria above as your checklist regardless of which vendor you're evaluating.
Pega Named a Leader in the 2026 Gartner® Magic Quadrant™ for BOAT Platforms. See why Pega was recognized as a Leader for the second year in a row. Read the report →
- Camunda, "2025 State of Process Orchestration and Automation Report," January 22, 2025. https://www.businesswire.com/news/home/20250122975479/en/8-in-10-Organizations-Fear-Digital-Chaos-as-Business-Process-Complexity-Increases
- MIT Project NANDA (MIT Media Lab), Aditya Challapally et al., "The GenAI Divide: State of AI in Business 2025," July 2025.
https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf - PwC, "29th Annual Global CEO Survey," January 2026.
https://www.pwc.com/gx/en/news-room/press-releases/2026/pwc-2026-global-ceo-survey.html - McKinsey, "State of AI Trust in 2026: Shifting to the Agentic Era," 2026.
https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/tech-forward/state-of-ai-trust-in-2026-shifting-to-the-agentic-era - McKinsey, "The State of AI: Global Survey 2026," September 2026.
https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai - Pegasystems (research conducted by Savanta), "Average Global Enterprise Wastes More Than $370 Million Every Year Through Technical Debt," October 14, 2025. https://www.businesswire.com/news/home/20251014412055/en/
Gartner, Inc. Magic Quadrant for Business Orchestration and Automation Technologies. Sailkat Ray, Arthur Villa, etl. 14 September 2026.
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