AI agents can go beyond the limits of traditional automation to handle complex knowledge work that, up until now, had to be done by people. This promises efficiency gains, better employee experiences, and faster service for customers. Some organizations are already experiencing benefits; according to a PwC survey from May 2025, 66% of senior executives surveyed say that AI agents are “delivering measurable value through increased productivity.”
However, there are still many challenges ahead. While organizations across industries are conducting proofs of concept for the use of AI agents, most agents that make it out of the experimentation phase remain limited, siloed, and mistrusted. According to Capgemini’s research, “Only 27% of organizations express trust in fully autonomous AI agents, from 43% 12 months ago. This is potentially a reflection of business reality taking hold after the initial enthusiasm and overconfidence in agentic AI capabilities.”
The reality is, most organizations aren’t trusting agents with mission-critical work today. Instead of making them part of orchestrated, end-to-end business processes, teams keep them siloed as a way to preserve a high level of human control.
The relationship between process orchestration and agentic orchestration
For organizations that are successfully delivering or unlocking additional value through agentic AI, a key success factor is process orchestration. Process orchestration enables teams to coordinate the tasks that make up an end-to-end business process in a single process flow across endpoints: AI agents, APIs, enterprise applications, and knowledge workers.
Most process orchestration is deterministic, meaning that it uses predefined logic to execute a process. Deterministic orchestration is predefined, auditable, and ideal for structured processes with clearly defined steps and outcomes. On the other hand, AI agents can now add a dynamic aspect to deterministic processes. Dynamic orchestration leverages agents to determine the next steps to take as a process is being executed. It introduces flexibility by allowing agents to adapt to real-time context.
Agentic orchestration blends these approaches by allowing deterministic process logic to govern known, repeatable paths while delegating unpredictable paths to AI agents that are embedded within the process. To implement true agentic orchestration, teams create this blend using features that are native to the process orchestration platform; not a separate system or tool. This native blend ensures that the integrity and context of the business process are maintained while combining the precision of deterministic design with the adaptability of dynamic actions.
How agentic orchestration enables autonomy and trust
The combination of deterministic and dynamic orchestration enables teams to build controls into AI agents’ internal algorithms, ensuring their behavior is built for trust and increased autonomy from the very start. A solution that enables this type of design provides teams with granular control over how the agent uses dynamic dynamic orchestration to handle unpredictable situations, and how it uses deterministic orchestration to execute recurring patterns and enforce guardrails.
Agentic orchestration delivers the predictability and guardrails that are needed to build trust in AI agents while preserving the autonomy that makes agents intelligent and agile enough to handle complex knowledge work.
What matters when choosing solution
To be successful with agentic orchestration, you need a solution that delivers the following:
- End-to-end process orchestration: The solution can orchestrate every automated and manual task in an end-to-end business process across endpoints such as AI agents, APIs, RPA bots, and microservices; as well as tools for customer relationship management (CRM), enterprise resource planning (ERP), and IT service management (ITSM).
- Agentic orchestration: The solution can combine deterministic orchestration (where every process step is predefined) with dynamic orchestration (where AI agents make real-time decisions based on goals and context) in the same process using a shared process model.
- Support for long-running processes: It has technical capabilities such as state persistence, which are needed to manage business processes that run for hours, days, weeks, or even longer.
- Process analytics and optimization: It provides actionable insights based on real-time and historical process data, with features that help teams identify bottlenecks, analyze performance issues, and continuously improve processes.
- One model approach: It uses a consistent process model visualization across design, monitoring, and improvement activities.
- Standards-based collaboration: It uses open standards to facilitate communication and alignment between business technologists, low-code developers, professional software developers, and anyone else who is a stakeholder for business processes.
- Developer friendliness: It doesn’t require software developers to adopt a vendor-specific way of working, but instead meets them in their comfort zone by fitting into both their development toolkit and application architecture.
- Flexible architecture: Its components are designed to work together seamlessly but can also be used independently, with easy integration into enterprise tech stacks. It offers on-premise, cloud, and hybrid deployment options.
- Low total cost of ownership: Open standards, developer friendliness, and flexible architecture combine to reduce the implementation cost for automation projects and keep ongoing maintenance and improvement costs low.
- Highly scalable and resilient: It leverages cloud-native technology to scale process volumes and to provide resilience against unplanned downtime and data loss.
Comparing Camunda to other solutions
In the updated Camunda Compared to Alternatives paper, we dive into how Camunda stacks up to other solutions that partially deliver these capabilities, including:
- Process automation tools
- Monolithic automation platforms, from traditional monoliths to modern, all-in-one platforms
- Microservices orchestrators
- Integration platforms as a service (iPaaS)
- Task automation tools including robotic process automation (RPA) platforms, intelligent document processing (IDP) tools, and AI task agents
To learn more, download your free copy of Camunda Compared to Alternatives today.
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