Single copilots are yesterday’s 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’s AI Impact Series, presented by SAP in San Francisco, tackled the issue of deploying and governing multi-agent AI systems.
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’s goal is to ensure that customers can scale their AI agents, but safely, Oren said.
“You 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,” he said. “This technology needs to be monitored at scale. It’s not perfect yet. This is the tip of the iceberg around what we’re doing to make sure that agents can scale, and also minimize any vulnerabilities.”
Deploying active AI pilots across the organization
Right now, Agilent is actively integrating AI across the organization, Jampa said. The results are promising, but they’re still in the process of tackling those vulnerability and scaling issues.
“We’re in a stage where we’re seeing results,” he explained. “We’re now having to deal with problems like, how do we enhance monitoring for AI? How do we do cost optimization for AI? We’re definitely in the second stage of it, where we’re not exploring anymore. We’re looking at new challenges and how we deal with these costs and monitoring tools.”
Within Agilent, AI is deployed in three strategic pillars, Jampa said. First, on the product side, they’re exploring how to accelerate innovation by embedding AI into the instruments they develop. Second, on the customer-facing side, they’re identifying which AI capabilities will deliver the greatest value to their clients. Third, they’re applying AI to internal operations, building solutions like self-healing networks to boost efficiency and capacity.
“As we implement these use cases, one thing that we’ve focused on a lot is the governance framework,” Jampa explained. That includes setting policy-based boundaries and ensuring the guardrails for each solution remove unnecessary restrictions while still maintaining compliance and security.
The importance of this was recently underscored when one of their agents did a config update, but they didn’t have a check in place to ensure its boundaries were solid. The upgrade immediately caused issues, Jampa said — 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.
Adding a human layer is the last piece.
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