Confidence in Agentic AI: Why Evaluation Infrastructure Must Come First

The rise of artificial intelligence (AI) agents in real-world deployment has marked a new era in technological innovation. Businesses are increasingly realizing the tremendous potential of AI agents to transform operations, optimize efficiency, and generate substantial savings. However, with these benefits comes the challenge of ensuring that AI agents operate reliably and accurately. This is where evaluation infrastructure becomes critical.

The Growing Role of AI Agents

AI agents are sophisticated software entities designed to perform specific tasks that traditionally required human intervention. Their initial appeal often lies in cost savings and increased productivity. As Shailesh Nalawadi, VP of Project Management at Sendbird, points out, the transformative power of AI agents goes beyond mere cost-saving; they represent a fundamental shift in how tasks can be automated and optimized, leading to far-reaching impacts on business processes (VentureBeat).

Take Rocket Companies, for instance. Their AI agents not only enhanced website conversion rates but were pivotal in automating specialized tasks like mortgage underwriting calculations, saving the company a million dollars a year in expenses (VentureBeat). Such achievements highlight how AI agents can supercharge productivity by performing mundane, time-consuming tasks.

Tackling the Complexity of AI Agents

Integrating AI into operational processes is not without its challenges. AI agents must transition from being simply programmed to delivering varied responses based on probabilistic insights derived from large language models (LLMs). This shift requires an evolved mindset in software engineering teams, as they adapt to the non-deterministic nature of LLMs (Managing the non-deterministic nature of generative AI).

Today’s AI systems can combine and orchestrate models to enhance their responsiveness and ensure they perform optimally under varied conditions. As Thys Waanders, SVP of AI transformation at Cognigy, explains, the challenge is now in model orchestration and ensuring seamless performance across massive scales of operation. The technology and infrastructure must evolve constantly to support this dynamic environment (MCkinsey).

Tapping into Vendor Relationships

Creating a conducive environment for AI development often means looking beyond in-house capabilities. Companies need specialized expertise to build and maintain robust AI infrastructures. Successful AI transformations frequently involve vendors who can offer advanced solutions, allowing businesses to focus on differentiation rather than the intricacies of AI architecture (Harvard Business Review).

Nalawadi highlights that many firms need to iterate beyond a basic product (1.0) to stay competitive, thus requiring skilled partners who can align technological advancements with organizational goals (VentureBeat).

Preparing for AI Complexity: The Role of Evaluation Infrastructure

Agentic AI’s promise is vast but so are its complexities. Enterprises must prepare for a landscape where AI systems growing in scale and function require comprehensive checks and balances. Here, an evaluation infrastructure is indispensable. It acts as the unit testing framework for AI systems, ensuring agents operate within expected parameters, even as they evolve (ZDNet).

The evaluation infrastructure should simulate conversations across multiple scenarios to identify potential operational pitfalls, thereby preventing unexpected behavior in real-world deployments. As Shawn Malhotra, CTO at Rocket Companies, suggests, it involves ensuring humans remain in the loop to verify and validate critical AI decisions. A system for detailed monitoring and alerting is necessary to catch and rectify errors (IBM).

Conclusion

For organizations considering the journey towards AI integration, defining a robust evaluation infrastructure is the first critical step. It not only ensures the reliability of AI systems but also supports scalability and evolution in AI agents’ function and application. Companies like Encorp.ai can provide expert consultancy and solutions tailored to the complex requirements of agentic AI, thus promising efficient integration and deployment strategies to enhance business capabilities in this AI-driven future.

References

1. VentureBeat on Agentic AI
2. Forbes - Embracing AI Potential
3. MIT Technology Review - Scaling AI Agents
4. Harvard Business Review - Vendor Relations
5. ZDNet - Evaluation Infrastructure