In regulated industries, relying on an AI agent's "reasonable decision" without proper traceability is a risky gamble. For audit purposes, organizations need explicit evidence: who authorized actions, what controls applied, and crucially, where and when a human could intervene. This demand for transparency and governance shapes how workflows involving AI and humans are architected.

Two platforms offering solutions at this intersection are Camunda and Diagrid Catalyst. While both support AI workflows with human approval, they originate from fundamentally different design principles and target control boundaries. Camunda centers on BPMN-based end-to-end process orchestration, making every step explicit and transparent. Diagrid Catalyst approaches the problem from durable, governed AI agent execution, emphasizing recovery, identity, and observability at the agent runtime level.

This post unpacks these differences and explores key criteria to decide which platform to choose—or how to combine them—when building AI workflows that must pass human approval in regulated environments. Along the way, you'll gain insight into the architectural visions behind Camunda and Catalyst, helping you select the right tool for your compliance and operational needs.

Key Technical Observations

• Distinct Control Boundaries Define the Product Fit: Camunda emphasizes end-to-end BPMN process orchestration, creating a single process model both tech and business teams collaborate on. Diagrid Catalyst focuses on managing durable AI agent workflows, preserving identity and execution lineage at runtime—ideal for complex AI agents interacting dynamically with tools or microservices.

• Audit Requirements Go Beyond Diagrams and Logs: In regulated workflows, providing a visual process map or standard logs is insufficient. It's critical to prove execution history, identity of approvers, authorization records, and immutable evidence of the decision path. Catalyst’s verifiable execution is designed around meeting such compliance needs explicitly, not merely as a checkbox.

• Non-Determinism Placement Drives Architecture: If AI is a bounded task within a stable, deterministic BPMN process, Camunda's approach fits well. Conversely, if AI agents must dynamically decide their next actions at runtime—potentially calling external services or delegating tasks—Catalyst’s durable agent runtime excels.

• Ownership and Tooling Influence Platform Choice: When business analysts and process owners need visibility and control through BPMN models, Camunda naturally leads. Where developers own agent code and prefer to work within familiar code-first frameworks, Catalyst’s runtime compatibility and developer-oriented governance is advantageous.

• Deployment and Regulatory Topology Matter: Both platforms offer multiple deployment options, but organization policies around SaaS, private infrastructure, private connectivity, or isolated environments must be verified specific to each tool and edition, ensuring compliance with internal regulations.

• Hybrid Architectures Are Valid and Common: Enterprises can combine both platforms, using Camunda for orchestrating human workflows and high-level process visibility, while delegating complex AI agent execution to Catalyst or vice versa. This layered approach aligns responsibilities with organizational domains (business vs. development).

How It Works: Deep Dive into AI Workflow Governance

Camunda: Explicit BPMN Process Orchestration with AI and Human Tasks

Camunda extends BPMN workflows by integrating AI agents as first-class participants within explicit end-to-end processes. A typical insurance claim workflow might look like this:

1. Deterministic Steps: Validate policy coverage, fetch customer data, and check claim eligibility—handled by deterministic rules and service tasks.
2. AI Task: An AI agent summarizes claim evidence, flags anomalies, and generates a recommendation embedded as an AI decision task.
3. Human Approval: A human task policy owner approves or rejects the AI’s recommendation, providing the necessary audit trail with identity and timestamps.
4. Payment Execution: Upon approval, a payment service task changes system state accordingly.

Camunda’s BPMN-centric design shines where business analysts need to maintain and communicate the process, supported by detailed documentation on agentic orchestration.

Diagrid Catalyst: Durable AI Agent Runtime with Governance and Observability

Catalyst treats AI agents as durable, governed workflows with strong runtime guarantees:

• Durability: Agents persist execution state to enable recovery and resumption from any failed step.
• Identity & Policy Enforcement: Agents run under specific workload identities with strict policy controls on tool usage, delegation, and side effects.
• Observability & Auditability: Execution history, authorization decisions, and lineage are recorded in a verifiable manner to satisfy compliance.

This approach is ideal when an AI agent is not a single task inside a defined process but a multi-faceted actor that can:

• Call external tools or other agents.
• Run in different environments or clouds.
• Execute asynchronously over extended periods.

Developers often embed Catalyst workflows within their existing frameworks, preserving flexibility and control over AI behavior across distributed systems.

// Example (pseudocode) of Catalyst durable agent step execution:
{
  "agentId": "claim-assessor-123",
  "state": "awaiting-approval",
  "history": [
    {"step": "summarize-evidence", "result": "flagged-anomaly", "timestamp": "..."},
    {"step": "await-human-approval", "authorizedBy": "user-456", "timestamp": "..."}
  ],
  "nextStep": "execute-payment"
}

Quick Tips & Tricks

1. Engage Business Analysts Early with BPMN Models
   Use Camunda if your process requires clear visibility and modification by non-technical stakeholders.

2. Map AI Non-Determinism Boundaries Upfront
   Decide if your AI is a discrete task or a dynamic agent workflow. This decision directs the platform choice.

3. Leverage Durable Execution for Long-Running AI Agents
   Use Catalyst’s durable workflows when AI tasks may pause, interact with external systems, and resume reliably without data loss.

4. Ensure Audit Trails Include Authorization Metadata
   Record not just decisions but who authorized them, applying strict identity and policy governance at execution time.

5. Combine Platforms When Needed
   Don’t feel forced to choose one. Use Camunda and Catalyst together to align with organizational roles and technical complexity.

6. Validate Deployment Topologies Against Compliance
   Confirm support for your approved infrastructure, especially if dealing with sensitive regulated data.

Conclusion

Building AI workflows that include human approval in regulated environments is a nuanced challenge. Camunda and Diagrid Catalyst offer complementary strengths tailored to different governance priorities and design models. Camunda excels where explicit process orchestration and transparency for business teams are paramount. Catalyst shines when durable, dynamic AI agents require governance, resilience, and fine-grained control.

Ultimately, your platform choice hinges on clear answers about who owns the process, how non-determinism is managed, what compliance artifacts must be produced, and where the solution will run. Many successful implementations combine both tools to deliver robust AI workflows that satisfy auditors, developers, operators, and business owners alike.

As AI agents become widely adopted in mission-critical workflows, expect demand to rise for systems that deliver not only smart automation but also rigorous human-in-the-loop governance, auditable controls, and durable execution. Camunda and Diagrid Catalyst address these needs from different directions—understanding their differences is critical to building trusted AI workflows that scale.

References

1. Camunda or Diagrid Catalyst for AI Workflows With Human Approval? - DEV Community — Original analysis article
2. Camunda Agentic Orchestration — Official Camunda documentation on AI workflow extension
3. Diagrid AI Agents — Core concepts of Diagrid’s AI agent model
4. Operating Catalyst — Details on Catalyst deployment, governance, and observability