Enterprise Agentic Automation
Build for trusted AI autonomy
Camunda’s enterprise agentic automation platform lets you design, govern, and scale agentic systems you can trust: where deterministic guardrails and dynamic AI work together inside a single end-to-end business process.
Agents have hit a ceiling
While agentic AI promises to remove the limits of traditional automation and automate complex knowledge work, agents have already hit a ceiling because they’re often:
Task-focused
Not built to deliver mission-critical outcomes
Siloed
Disconnected from people, systems, and other agents, and not ready to scale across the enterprise
Mistrusted
Lacking the guardrails and governance required for high-value work
There’s a gap between agentic promise and production reality
Most organizations have piloted agents, RAG tools, and domain-specific copilots, yet struggle to scale beyond siloed, task-level use cases. The result: limited autonomy, brittle integrations, and low trust for mission-critical work.
What’s needed for enterprise-grade agents
- Resilient orchestration that’s stateful, scalable, and fault-tolerant for long-running agents and processes
- Composable integration that connects people, systems, and agents across business processes
- Flexibility and reusability so process owners can dial agent autonomy up or down as needed
- Guardrails and auditability that enforce compliance and eliminate black-box behavior
- Operational visibility so you can monitor agent performance, costs, and outcomes
See how to make agentic orchestration work in your automation strategy
“Agentic and all the AIs are getting better so much more quickly than we can anticipate. So, by remaining on the leading edge together with Camunda and being here to remain at the forefront will really help us in using automation and IT to its fullest potential and keeping us on the leading edge of the business that we want to do.”
André Bal, Director of Supply Chain and Automation, SRE
Payter
Enterprise agentic automation
Get significantly higher value from AI with a solution that provides the guardrails needed to trust in AI’s autonomy, enabling deployment across end-to-end and mission-critical business processes.
Agent development
Build enterprise-grade agents with guardrails so that they can solve complex problems with autonomy
Agent orchestration
Coordinate multiple agents and people together to achieve desired outcomes
Case and process automation
Deliver end-to-end automation for structured and semi-structured work
Building enterprise-grade agents with Camunda
- Scale from agents to processes. Move beyond isolated agents to orchestrate multiple agents and humans across transparent, end-to-end processes, without creating black boxes.
- Design agents for autonomy and trust. Specify where AI adapts dynamically and where deterministic steps enforce policy, compliance, and human-in-the-loop checkpoints (e.g., approvals before fund transfers).
- Deliver the vision of an autonomous enterprise. Trusted, enterprise-grade agents collaborate with people and systems to drive holistic processes—governed, observable, and resilient.
Overcome the AI trust gap
Gain visibility, control, and auditability across agents and business processes.Avoid technical debt
Unify deterministic and dynamic patterns, reuse process building blocks, and avoid agent sprawl.
Accelerate time to value
Camunda users typically move their first business-critical agents to production in under three months.
“In healthcare, regulatory compliance, auditability, and predictable outcomes that can adapt quickly form the foundation of trust we hold with patients, providers, and regulators alike. Camunda’s orchestration gives us that solid foundation—providing the stability we need while still enabling us to push forward with GenAI-driven innovations that improve care and operational efficiency.”
Terry Camerlengo, Director of Technology
Wellpointe
Agentic Orchestration for Innovation Leaders: See how to operationalize AI
Blend deterministic and dynamic orchestration for assurance and autonomy
Camunda’s unique use of the BPMN process modeling standard for agent design and development delivers maximum flexibility by allowing IT and business teams to build deterministic flows around and inside AI-powered agents.
Why Camunda for enterprise agentic automation?
Put deterministic guardrails on dynamic AI increase control and prevent undesired actions
Use built-in operations and optimization to monitor agent performance, detect bottlenecks, track LLM token costs, and extract deterministic patterns to improve cost and performance
Change agents quickly and safely by updating agents without disrupting in-flight processes
Leverage prebuilt connectors for OpenAI, Google Gemini, Hugging Face, Glean, AWS Bedrock, Amazon SageMaker, and more
Collaborate on a shared BPMN process model that software developers and business stakeholders can build, change, and understand
Frequently asked questions
What is enterprise agentic automation?
Enterprise agentic automation (EAA) is the discipline of designing, governing, and scaling AI agents as part of mission-critical business processes. Instead of treating agents as isolated copilots or chatbots, you embed them in end-to-end business processes that also involve people and systems, with deterministic guardrails around dynamic AI behavior. Agents use planning loops, tools, and memory to solve complex problems, while the process orchestrator enforces policies, SLAs, and human-in-the-loop checkpoints where needed. The result is AI autonomy you can actually trust in production. Enterprise agentic automation closes the gap between experimental agents and real outcomes by providing resilience, composable integration, visibility into performance and costs, and full auditability across long-running processes.
What are enterprise-grade agents?
Enterprise-grade agents are AI agents that are explicitly designed for reliability, compliance, and scale inside business-critical processes. They do more than respond to prompts; they follow a modeled process, coordinate with systems and humans, and operate within guardrails that define what they can decide autonomously and where deterministic logic or approvals are required. Enterprise-grade agents typically use planning loops, tools, and retrieval to break down work into steps, apply short-term and long-term memory, and adapt to changing context, while every action is observable and auditable. In Camunda, these agents live inside BPMN-modeled business processes, so you can reuse components, prevent agent sprawl, and continuously improve behavior based on operational metrics such as success rates, bottlenecks, and token costs.
What are the typical use cases for enterprise agentic automation?
Enterprise agentic automation applies wherever AI needs to take on complex, end-to-end work rather than isolated tasks. Typical use cases include financial and insurance processes such as claims handling, transaction monitoring, and trade exception management, where agents can gather data, interpret documents, and recommend or execute actions while the orchestrator manages approvals and regulatory checks. In customer operations, enterprise-grade agents can drive case management, intelligent service requests, and personalized outreach, coordinating with customer relationship management (CRM), billing, and ticketing systems. In supply chain and healthcare, agents can detect exceptions, propose remediation options, and help manage semi-structured cases over days or weeks. Across all of these, Camunda provides the resilient, long-running orchestration, guardrails, and observability required to move from pilots to production at scale.
What are the key components or architecture behind an agentic automation solution?
An agentic automation solution typically includes several core components that work together. First, a resilient process orchestrator (such as Camunda’s Zeebe workflow engine) manages long-running, stateful processes and event-driven flows. Second, an agent layer provides LLM-based reasoning, planning loops, tools, and memory, often using techniques such as retrieval-augmented generation (RAG) to ground agents in enterprise data. Third, a composable integration layer connects systems, humans, and agents through APIs, RPA bots, MCP, A2A, and other connectors. Fourth, a governance layer defines policies, guardrails, role-based access, and human-in-the-loop checkpoints. Finally, an operations and optimization layer delivers monitoring, tracing, and analytics, including LLM token cost tracking, outcome metrics, and continuous improvement of deterministic and agentic steps. This architecture turns AI agents into reliable building blocks of end-to-end processes instead of opaque black boxes.
How does Camunda enable control of the autonomy level in AI agents?
Camunda lets you explicitly design how much autonomy an agent has in a business process by modeling a control flow in BPMN. You can surround agent tasks with deterministic steps, business rules, and decision gateways, then specify where human review, multi-factor checks, or secondary agents are required before an action is executed. For high-risk actions such as fund transfers or sensitive notifications, you can introduce human approval tasks and require multiple confirmations. For low-risk actions, you can let agents operate with more freedom and use monitoring to catch anomalies. Because every step is executed by Camunda’s workflow engine, Zeebe, you get full audit trails, event logs, and metrics on success rates and token usage. Over time, you can safely “dial up” autonomy in selected paths as confidence, guardrails, and testing mature.
How can we use Camunda to get started with enterprise agentic automation?
To start with enterprise agentic automation in Camunda, pick a business process that already involves knowledge work and repetitive decision-making; for example, claims intake, exception handling, or case resolution. Model the process in BPMN, then identify a few areas where an AI agent can add value, like classifying documents, extracting data, or generating a first decision proposal. Use Camunda’s Marketplace of connectors to integrate LLM providers such as OpenAI, Google Gemini, and AWS Bedrock, and configure prompts, tools, and memory for your agents. Surround these agent tasks with deterministic guardrails, human approvals, and policy checks. After you deploy the process, you can monitor it using Camunda Operate. Camunda Optimize enables you to keep an eye on performance, track LLM token costs, and refine both the deterministic model and the agent behavior. From there, you can incrementally expand to multi-agent patterns and more autonomous paths.
