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Single copilots are yesterday\u2019s news. Competitive differentiation is all about launching a network of specialized agents that collaborate, self-critique, and call the right model for every step. The latest installment of VentureBeat\u2019s AI Impact Series, presented by SAP in San Francisco, tackled the issue of deploying and governing multi-agent AI systems.<\/p>\n
Yaad Oren, managing director SAP Labs U.S. and global head of research & innovation at SAP, and Raj Jampa, SVP and CIO with Agilent, an analytical and clinical laboratory technology firm, discussed how to deploy these systems in real-world environments while staying inside cost, latency, and compliance guardrails. SAP\u2019s goal is to ensure that customers can scale their AI agents, but safely, Oren said.<\/p>\n
\u201cYou can be almost fully autonomous if you like, but we make sure there are a lot of checkpoints and monitoring to help to improve and fix,\u201d he said. \u201cThis technology needs to be monitored at scale. It\u2019s not perfect yet. This is the tip of the iceberg around what we\u2019re doing to make sure that agents can scale, and also minimize any vulnerabilities.\u201d<\/p>\n
Right now, Agilent is actively integrating AI across the organization, Jampa said. The results are promising, but they\u2019re still in the process of tackling those vulnerability and scaling issues.<\/p>\n
\u201cWe\u2019re in a stage where we\u2019re seeing results,\u201d he explained. \u201cWe\u2019re now having to deal with problems like, how do we enhance monitoring for AI? How do we do cost optimization for AI? We\u2019re definitely in the second stage of it, where we\u2019re not exploring anymore. We\u2019re looking at new challenges and how we deal with these costs and monitoring tools.\u201d<\/p>\n
Within Agilent, AI is deployed in three strategic pillars, Jampa said. First, on the product side, they\u2019re exploring how to accelerate innovation by embedding AI into the instruments they develop. Second, on the customer-facing side, they\u2019re identifying which AI capabilities will deliver the greatest value to their clients. Third, they\u2019re applying AI to internal operations, building solutions like self-healing networks to boost efficiency and capacity.<\/p>\n
\u201cAs we implement these use cases, one thing that we\u2019ve focused on a lot is the governance framework,\u201d Jampa explained. That includes setting policy-based boundaries and ensuring the guardrails for each solution remove unnecessary restrictions while still maintaining compliance and security.<\/p>\n
The importance of this was recently underscored when one of their agents did a config update, but they didn\u2019t have a check in place to ensure its boundaries were solid. The upgrade immediately caused issues, Jampa said \u2014 but the network was quick to detect them, because the second piece of the pillar is auditing, or ensuring that every input and every output is logged and can be traced back.<\/p>\n
Adding a human layer is the last piece.<\/p>\n
\u201cThe small, lowercase use cases are pretty straightforward, but when you talk about natural language, big translations, those are scenarios where we have complex models involved,\u201d he said. \u201cFor those bigger decisions, we add the element where the agent says, I need a human to intervene and approve my next step.\u201d<\/p>\n
And the question of speed versus accuracy comes into play early during the decision-making process, he added, because costs can add up fast. Complex models for low-latency tasks push those costs substantially higher. A governance layer helps monitor the speed, latency and accuracy of agent results, so that they can identify opportunities to build on their existing deployments and continue to expand their AI strategy.<\/p>\n
Integration between AI agents and existing enterprise solutions remains a major pain point. While legacy on-premise systems can connect through data APIs or event-driven architecture, the best practice is to first ensure all solutions operate within a cloud framework.<\/p>\n
\u201cAs long as you have the cloud solution, it\u2019s easier to have all the connections, all the delivery cycles,\u201d Oren said. \u201cMany enterprises have on-premise installations. We\u2019re helping, using AI and agents, to migrate them into the cloud solution.\u201d<\/p>\n
With SAP\u2019s integrated tool chain, complexities like customization of legacy software are easily maintained in the cloud as well. Once everything is within the cloud infrastructure, the data layer comes in, which is equally if not more important.<\/p>\n
At SAP, the Business Data Cloud serves as a unified data platform that brings together information from both SAP and non-SAP sources. Much like Google indexes web content, the Business Data Cloud can index business data and add semantic context.<\/p>\n
Added Oren: \u201cThe agents then have the ability to connect and create business processes end-to-end.\u201d<\/p>\n
While many elements factor into the equation, three are critical: the data layer, the orchestration layer, and the privacy and security layer. Clean, well-structured data is, of course, crucial, and successful agentic deployments depend on a unified data layer. The orchestration layer manages agent connections, enabling powerful agentic automation across the system.<\/p>\n
\u201cThe way you orchestrate [agents] is a science, but an art as well,\u201d Oren says. \u201cOtherwise, you can have not only failures, but also auditing and other challenges.\u201d<\/p>\n
Finally, investing in security and privacy is non-negotiable \u2014 especially when a swarm of agents is operating across your databases and enterprise architecture, where authorization and identity management are paramount. For example, an HR team member may need access to salary or personally identifiable information, but no one else should be able to view it.<\/p>\n
We\u2019re headed toward a future in which human enterprise teams are joined by agent and robotic team members, and that\u2019s when identity management becomes even more vital, Oren said.<\/p>\n
\u201cWe\u2019re starting to look at agents more and more like they\u2019re humans, but they need extra monitoring,\u201d he added. \u201cThis involves onboarding and authorization. It also needs change management. Agents are starting to take on a professional personality that you need to maintain, just like an employee, just with much more monitoring and improvement. It\u2019s not autonomous in terms of life cycle management. You have checkpoints to see what you need to change and improve.\u201d<\/p>\n<\/p><\/div>\n