Insurance has outgrown traditional automation. Today’s complex operations demand smarter systems that can adapt in real time.
Enter agentic AI: artificial intelligence that can make autonomous decisions, adapt to changing conditions, and handle complex scenarios that previously required human intervention.
By 2028, at least 15% of day-to-day work decisions will be made autonomously through agentic AI, up from 0% in 2024, according to Gartner. For insurance companies, this represents both an opportunity and a challenge.
How do you harness this technology while maintaining the control and compliance requirements that define your industry?
How Camunda Enables Controlled AI Decision-Making
We partnered with Camunda to help our clients embed AI into real business processes without losing transparency or control.
In insurance, this means using agents for underwriting, claims processing, compliance monitoring, fraud detection, and customer service, but ensuring appropriate guardrails are in place.
The challenge with giving AI more autonomy is maintaining control and compliance. This is where Camunda's agentic AI integration becomes valuable.
Camunda enables you to integrate AI agents into your end-to-end processes through a specific workflow pattern that provides AI flexibility within defined boundaries.
At the heart of this approach is something called an ad-hoc subprocess. Think of this as a special container within your business process where AI can make its own decisions about what to do and in what order.
You can use an AI agent to select and execute tasks within an ad-hoc sub-process, by evaluating the current process context and determining the relevant tasks and tools to use in response.
Best Practices for Camunda Agentic AI Integration
Design Clear Boundaries for AI Decision-Making
The most successful implementations clearly define what decisions AI can make independently versus what requireshuman involvement. Within your process workflows, create specific zones where AI has autonomy and clear escalation points where human expertise takes over.
For example, an AI agent might have full authority to approve standard auto insurance renewals but automatically route applications with unusual risk factors to human underwriters. This boundary-setting ensures appropriate oversight while maximizing automation benefits.
Start with Flexible Process Containers
A common model for AI agent integration uses an ad-hoc sub-process and AI agent connector in a tools feedback loop. This involves creating process sections where AI can select from multiple available actions, rather than adhering to a single predetermined path.
Instead of building rigid "if-then" rules, design your processes to give AI agents a toolkit of actions they can select from based on what they encounter. This approach makes your automation more adaptable and intelligent.
Ensure Every Decision Can Be Explained
In regulated industries like insurance, every decision must be traceable and explainable. Design your AI integration to capture not just what decisions were made, but why they were made and what information influenced them.
The AI agent can understand the context and process the goal, and uses the available tools to complete the goal. Document this reasoning process to satisfy regulatory requirements and enable continuous improvement.
Build In Continuous Learning
AI agents improve over time when they can learn from outcomes. Design your processes to capture results and feed them back into your AI systems. This creates a cycle of continuous improvement where your automation becomes more accurate and effective with experience.
Implementation Patterns That Work
Start Small and Scale Gradually
Begin with processes that have clear success metrics and well-defined boundaries. Choose areas where AI can provideimmediate value like routine document processing or initial risk assessments, while maintaining human oversight for complex decisions.
Focus on one process at a time, prove the value, and then expand to additional areas. This approach builds organizational confidence while providing real-world data for refining your integration approach.
Establish Governance Early
Create clear policies for AI agent capabilities, limitations, and oversight requirements. Your governance framework should address compliance obligations, risk management, and quality assurance before you scale your implementation. Who has the authority to modify agent behavior? How will you monitor performance?
Why It’s Hard to Do Alone
Designing AI-driven workflows sounds straightforward in theory, but the reality is far more complex. Defining clear decision boundaries, managing compliance, capturing explainability, and ensuring ongoing learning all require deep experience in both process design and AI integration.
Most organizations don’t have the internal alignment or technical maturity to tackle this on their own.
That’s where we come in.
Building Your Future-Ready Organization
Insurance operations are becoming more complex, customer expectations are rising, and regulatory requirements continue to evolve. Companies that successfully integrate agentic AI with robust process orchestration will handle this complexity more effectively while their competitors struggle with rigid automation systems.
We at BP3 understand that implementing agentic AI is about transforming how your organization operates while maintaining the reliability and compliance that insurance demands.
Our partnership with Camunda enables us to deliver solutions that provide AI's autonomous capabilities within frameworks you can trust and control. And we can help you hit the ground running.
The future of insurance is autonomous, intelligent, and orchestrated. The question isn't whether agentic AI will transform your industry, it's whether you'll be ready when it does. Get in touch today.