Architecting Agentic AI Workflows With Confidential Computing

1. Architecting Agentic AI Workflows With Confidential Computing The rapid evolution of artificial intelligence has brought about new ways of designing systems that go beyond traditional models. One of the most promising directions lies in agentic workflows, where multiple AI agents collaborate to complete tasks autonomously. These agents operate as specialised units, each responsible for a specific function, and together they form a chain of intelligence capable of solving complex problems. However, with such power comes significant responsibility, particularly when handling sensitive or confidential data. This is where confidential computing plays a transformative role, enabling secure and trustworthy execution of these advanced workflows. In the past, AI systems were often limited by concerns around privacy and trust. Organisations wanted to deploy AI agents across sensitive data sets but were held back by risks of exposure, leakage, or misuse. Agentic workflows make these risks even more pronounced, since information often flows through multiple agents, each making independent decisions. Ensuring that the entire workflow operates within a secure environment is essential if the technology is to gain wider adoption in industries where compliance, regulation, and data protection are paramount. Confidential computing provides a foundation to address this challenge. By using hardware-based secure enclaves, confidential computing ensures that data remains encrypted not only at rest and in transit, but also during processing. This means that when AI agents perform their tasks within a confidential environment, sensitive information is shielded from external access, including from system administrators or cloud providers. The result is a framework where agentic workflows can run with strong guarantees of confidentiality. The interplay between agentic workflows and confidential computing allows AI to operate in contexts that were once deemed too risky. Consider a healthcare scenario where multiple AI agents work together to analyse patient data, recommend treatments, and coordinate with external services. Without strict security, this workflow would expose personal health records to unnecessary risks. With confidential computing, however, the entire pipeline is protected, ensuring that patients’ data remains confidential while still enabling powerful AI-driven insights. The same applies to financial services, where agentic AI might process sensitive customer information, conduct risk assessments, or even automate investment strategies. Trust in these workflows cannot be achieved through software controls alone. Confidential computing makes it possible to prove, both technically and cryptographically, that no unauthorised party has had access to the data. This assurance is critical in regulated industries where breaches could have far-reaching consequences. Agentic AI workflows are dynamic by nature, often requiring agents to communicate with one another and sometimes with external sources. This introduces the need for strong guardrails to ensure that information flowing between agents does not lead to policy violations or security breaches. Confidential computing complements this by enforcing secure boundaries around each agent’s operations, preventing unauthorised data extraction and offering verifiable proof of compliance. Architecting these workflows involves more than just plugging agents into a secure environment. It requires thoughtful design that considers how agents will communicate, how data will be shared, and how policies will be enforced at every stage. Confidential computing provides the underlying layer of trust, but developers must ensure that workflows are structured to minimise unnecessary data exposure, even within secure enclaves. One of the most exciting aspects of this approach is the ability to create compound AI systems. These systems combine multiple agents, each with unique strengths, to deliver outcomes that a single model could not achieve alone. When confidential computing underpins this structure, it becomes possible to orchestrate highly capable AI systems without compromising on data protection. This opens doors for use cases across government, legal, healthcare, and enterprise settings where sensitive data is the norm. As these systems grow in complexity, the need for transparency and auditability becomes increasingly important. Confidential computing allows for cryptographic attestation, which means organisations can verify that workflows ran exactly as intended and within secure boundaries. This level of accountability builds trust not only internally but also with customers and regulators. It marks a shift from simply promising secure AI to demonstrating it in verifiable ways. Another layer of value comes from integrating retrieval-augmented generation into agentic workflows. Retrieval-augmented generation, or RAG, enhances AI by grounding outputs in external knowledge bases. When combined with confidential computing, it ensures that sensitive information retrieved during the process remains encrypted and protected throughout. This combination strengthens both the accuracy and trustworthiness of agentic AI while safeguarding the underlying data. While technical infrastructure is crucial, cultural and operational shifts are also required for successful adoption. Organisations must align their teams around the principle that security and confidentiality are not optional extras but central pillars of AI design. Agentic workflows can only thrive when security is considered from the ground up, rather than as an afterthought once models are already in production. Challenges do remain, particularly around performance and cost. Running workloads inside secure enclaves introduces additional considerations, and scaling agentic workflows in confidential environments requires optimised engineering. However, these challenges are outweighed by the benefits of operating with strong security guarantees, particularly in industries where breaches can erode trust for years. The ethical dimension is also significant. AI agents making decisions on behalf of humans must do so with integrity and fairness. Confidential computing helps by ensuring that the data feeding these decisions remains accurate, untampered, and private. This protects individuals while reinforcing the legitimacy of AI-driven outcomes. It creates an environment where innovation and responsibility can coexist.

2. Looking ahead, the convergence of agentic AI and confidential computing is likely to become a cornerstone of enterprise AI strategy. As organisations demand both more powerful and more secure AI, these technologies provide the pathway forward. The result will be systems that not only perform complex tasks autonomously but also do so in a way that respects privacy, security, and compliance at every step. Policymakers and regulators are beginning to take note as well. By building frameworks that recognise the role of confidential computing, governments can encourage innovation while ensuring that citizens’ data remains protected. This alignment between technology and regulation will help establish consistent standards across industries and regions. Ultimately, the success of agentic AI workflows depends on trust. Users, customers, and stakeholders must believe that the technology is both capable and safe. Confidential computing provides the assurance needed to foster that trust, ensuring that the extraordinary potential of agentic workflows can be realised without compromising data protection. As AI continues to reshape industries, the union of agentic workflows with confidential computing offers a glimpse of a future where intelligence and security go hand in hand. It demonstrates that progress need not come at the expense of privacy and that with the right design principles, organisations can harness the full power of AI responsibly. About OPAQUE OPAQUE is a leading confidential AI platform that empowers organisations to unlock the full potential of artificial intelligence while maintaining the highest standards of data privacy and security. Founded by esteemed researchers from UC Berkeley's RISELab, OPAQUE enables enterprises to run large-scale AI workloads on encrypted data, ensuring that sensitive information remains protected throughout its lifecycle. By leveraging advanced confidential computing techniques, OPAQUE allows businesses to process and analyse data without exposing it, facilitating secure collaboration across departments and even between organisations. The platform supports popular AI frameworks and languages, including Python and Spark, making it accessible to a wide range of users. OPAQUE's solutions are particularly beneficial for industries with stringent data protection requirements, such as finance, healthcare, and government. By providing a secure environment for AI model training and deployment, OPAQUE helps organisations accelerate innovation without compromising on compliance or data sovereignty. With a commitment to fostering responsible AI adoption, OPAQUE continues to develop tools and infrastructure that prioritise both performance and privacy. Through its pioneering work in confidential AI, the company is setting new standards for secure, scalable, and trustworthy artificial intelligence solutions.