Federal agencies today face increasing pressure to modernize, reduce workforce dependency, and deliver faster, more efficient services. The Trump Administration’s 2025 AI Executive Orders—particularly EO 14179, M-25-21, and M-25-22—emphasize rapid AI adoption, vendor lock-in prevention, and agile alignment with evolving policies.
To meet these mandates—and to deliver citizen services with the personalization, speed, and ease expected from modern digital experiences—government agencies need more than isolated AI pilots or fragmented automation tools. They need an orchestration framework that connects AI services, automation, and human decision points into flexible, auditable, and policy-aligned workflows.
This post advocates for a lightweight, open, and scalable orchestration layer—powered by BPMN (Business Process Model and Notation) and DMN (Decision Model and Notation)—that connects automation, AI services, and human decision points into flexible, auditable, and policy-aligned workflows. It supports existing deterministic systems while enabling the dynamic, context-aware behavior required for agentic AI.
The challenge: Disconnected tools, fragile architecture
While most federal agencies have experimented with AI pilots using chatbots, document classification, and various RPA bots, these efforts often operate independently, lacking a cohesive execution framework.
Over time, different teams across agencies have deployed AI and automation technologies independently, often using bespoke tools or isolated solutions tailored to a single use case. These systems are rarely designed to interoperate, and as a result, they create silos rather than synergy.
This patchwork of automation contributes to a “value trap,” where isolated wins deliver local efficiencies but compound cross-agency complexity.
- Tools operate in isolation: limiting end-to-end visibility and decision-making.
- Human approvals and exception handling remain manual: disconnected from core automation.
- Every policy update becomes a rewrite: requiring time-consuming and costly code changes.
- Transparency is lost: as processes span disparate systems without centralized visibility.
These issues contribute to mounting technical debt and governance risk. According to Gartner, more than 50% of AI implementations will be reworked or replaced by 2027 as they become technical debt. For AI to become transformative rather than tactical, agencies need a process execution backbone.
Why BPMN is the foundation and execution Layer
Historically, government processes have been modeled and executed in a deterministic fashion, defined by rigid, step-by-step logic. But the introduction of agentic AI introduces dynamic behavior: the ability to make decisions in real time, respond to new inputs, and evolve without rewrites.
This requires a new kind of orchestration, one that can blend structured, policy-driven rules with flexible, contextual decision-making.
BPMN is not just a visual modeling language. It is the federal-grade blueprint for orchestrating AI, humans, systems, and policy into cohesive, explainable processes.
In agentic orchestration, where AI agents make decisions in real time, BPMN provides the structure that:
- Encodes policy logic in human-readable diagrams
- Incorporates human-in-the-loop and human-in-command designs
- Enables explainability for audits, oversight, and transparency mandates
- Seamlessly integrates deterministic rules and dynamic agent behavior
With BPMN:
- AI agents gain context: They don’t just act, they act within policy-defined flows.
- Humans maintain authority: Every task, exception, and override is explicitly modeled.
- Change becomes manageable: When a new EO or directive lands, IT and policy teams collaborate to easily evolve the process to align with new requirements.
Camunda natively executes BPMN models, giving agencies a standards-based, policy-aligned execution layer that supports AI, human decision points, and mission-critical systems.
The agentic advantage: From static to adaptive automation
The mandate has shifted. The pressure is not only to adopt AI, but to deliver truly agentic AI: systems that make decisions, adapt in real time, and provide citizens with a level of speed, personalization, and reliability on par with modern consumer experiences.
To meet that expectation, agencies must scale automation—securely and accountably. That means building with guardrails: human-in-the-loop controls, policy enforcement via DMN, audit logs, and override paths. Camunda provides these by default.
Traditional workflows are static. They follow predetermined paths. But government work is rarely predictable.
Agentic orchestration enables workflows that adapt in real time. Camunda supports this with:
- Dynamic agent tasks: that select tools or paths at runtime
- Guardrails via DMN: to enforce policy constraints
- Ad-hoc sub-processes: to respond to emerging scenarios
- Full observability: via Camunda Operate for real-time monitoring and control
This is not automation for automation’s sake. It’s process intelligence that evolves with mission needs.
Using agentic orchestration for eligibility determination
Imagine a benefit eligibility process:
- AI agent classifies intake documents
- BPMN-based workflow evaluates confidence score
- Low confidence? Route for human review
- High confidence? Trigger automated decision + fraud check
- Policy changes? Update/Add decision rules using DMN directly into the process model
- Camunda Operate monitors success and troubleshoots issues
- Camunda Optimize dashboards provide business oversight and analytics for improvement
This is not theoretical. It’s already in use by many federal-aligned teams.
Compliance and governance by design
Camunda’s BPMN-based process orchestration and automation directly aligns with federal directives:
| Policy Directive | Requirement | How Orchestration Supports It |
| EO 14179 | Accelerate AI, remove regulatory barriers, AI action plans, responsible AI adoption | Business-led, transparent process design; quick rollout without rewriting code |
| M-25-21 | Establish Chief AI Officers, AI governance, mandate AI maturity assessments | BPMN models document processes and enable risk assessment, showing AI use and human oversight |
| M-25-22 | Prevent vendor lock-in, favor American-made tools, monitor AI performance | Open orchestration avoids lock-in, and integrates with any AI vendor |
| DOGE Directives | Centralize data access, reduce manual work, reduce spending | Orchestration coordinates AI, bots, and human tasks, minimizing manual steps while optimizing orchestration footprint and spend |
Why not code it all? Why not use SaaS?
| Approach | Strengths | Federal Limitations |
| All-code (e.g. scripts, AWS Step Functions) | Flexible | No explainability, high dev cost |
| Big SaaS (e.g. Salesforce, ServiceNow) | Fast setup | Vendor lock-in, rigid models |
| Open BPMN-based orchestration | Transparent, auditable, flexible | Requires modeling discipline |
Camunda’s orchestration engine is horizontally scalable, audit-ready, and compliance-ready. Others simulate orchestration. Camunda executes it.
The execution layer for a policy-first AI era
The future of public sector AI isn’t just about intelligence, it’s about control.
BPMN-based agentic orchestration is how agencies align automation with ever evolving policies, human authority, and public trust. Camunda offers that execution layer—open, flexible, scalable.
Agencies don’t need to rewrite everything. They need to orchestrate everything.
BPMN makes it possible. Camunda makes it real.
Contact us to explore how open, agentic orchestration can operationalize your AI strategy and ensure mission success.
Start the discussion at forum.camunda.io