"Having the insight and being able to stitch together your technical resources and business decisions together, is the prime place where observability can add value to you,” stated Manesh Tailor, EMEA Field CTO at New Relic.

In this episode of the Tech Transformed podcast, Kevin Petrie, Vice President of Research at BARC, speaks with Manesh Tailor about the intersection of artificial intelligence (AI) and observability, and how this is positively changing business operations.

Tailor emphasises how intelligent observability has changed beyond simple monitoring to provide real-time insights into customer experience and the entire technology stack. This enables informed decisions across engineering, operations, and business domains, directly linking technical performance to strategic business outcomes.

He also discusses the different stages observability has been through and where it's leading to now. The current wave, Observability 3.0, takes advantage of AI to predict issues and even enable self-healing systems.

New Relic has embraced this two-way street, using AI within its platform. This was in an ambition to help users and "AI monitoring" to track the performance of language models alongside traditional metrics. Such a platform provides a holistic view of system health and the cost implications of AI deployments.

Alluding to the management of AI-powered applications, Tailor says collaboration is key between application and data science teams. Not only does it provide real time data but as a result leads to efficient decision making.

Futuristically, the speedy proliferation of AI agents has both pros and cons for observability. This is where New Relic comes in. It addresses the challenges by constructing a platform-centric "AI orchestrator" with a growing library of AI-native agents.

In essence, as AI-powered applications become increasingly integral to business operations, intelligent observability is no longer optional.

• Observability is crucial for understanding unknowns in systems.
• AI enhances observability by providing predictive insights.
• The evolution of observability includes intelligent monitoring.
• Collaboration between technical and business teams is essential.
• Cost efficiency is a key focus in modern observability.
• Real-time data is vital for effective decision-making.
• Self-healing systems represent the future of observability.
• AI and observability must work in tandem for success.
• The complexity of systems is increasing, requiring better tools.
• Observability is applicable across all organizational levels.

00:00 Introduction to AI and Observability

03:10 Defining Observability and Its Evolution

05:49 The Role of AI in Observability

08:46 Navigating AI-Driven Applications

11:52 Target Users and Community for Observability

14:57 Collaboration Across Teams

17:55 Challenges and Opportunities in Observability

20:47 The Future of Observability and AI

23:54 Key Takeaways for CIOs and IT Leaders

The New Relic Intelligent Observability Platform empowers businesses to proactively eliminate disruptions in their digital experiences. As the only AI-enhanced platform that unifies and correlates telemetry data, New...

Takeaways

• #AIagents are #autonomous entities that can perceive and act.
• Human oversight is essential in the initial stages of AI implementation.
• Data quality and trust are critical for effective AI agents.
• Guardrails must be integrated into the design of AI agents.
• Modularity in design allows for flexibility and adaptability.
• #AI should be embedded in data management processes.
• Collaboration between data and application teams is vital.

Summary

In this episode of #TechTransformed, Kevin Petrie, VP of Research at BARC, and Ann Maya, EMEA CTO at Boomi, discuss the transformative potential of AI agents and intelligent automation in business. They explore the definition of agents, their role in automating processes, and the importance of human oversight.

Maya introduces us into the world of AI agents stating that, at its core, it’s an autonomous entity within #AIsystems that can perceive its environment. This creates a deep dive into how they evolved from traditional automation to “observe, think, and act” in novel and autonomous ways.

Maya addresses AI skepticism by acknowledging its growing autonomy while underscoring the current necessity of human oversight. She also highlights data's crucial influence on an agent's perception and decisions, emphasising the need for quality, trustworthy data in effective AI.

Moreover, Maya and Petrie explore AI's practical implications, pointing to Google's agent-to-agent protocol as vital for managing language model interactions and enabling effective communication across diverse agents within complex systems.

"If AI has proven anything, it will change pretty rapidly. Understanding its limitations and not asking too much of it is significant. What’s successful is prototyping tools," said Rob Whiteley, CEO of Coder. "Such tools where AI can create an application, while not the world's most graceful code but will get you to working prototype pretty quickly. That would probably take me days or weeks of research as a developer, but now I have a working prototype so I can socialise it."

In this episode of the Tech Transformed podcast, Dana Gardner, a thought leader, speaks with Rob Whiteley, CEO of Coder, about the transformative impact of agentic AI on software development. They discuss how AI is changing the roles of developers, the cultural shifts required in development teams, and the integration of AI agents in cloud development environments.

Agentic AI is seemingly set up for favourable outcomes. Or is it? Agentic AI is believed to shake-up enterprise IT, offering a productivity boost similar to the iPhone's impact.

This isn't about replacing developers but amplifying their output tenfold. It aims to allow the implementation of rapidly created solutions and iteration that has been unimaginable in the past. This shift requires valuing "soft skills" like communication and collaboration over pure coding proficiency, as developers guide AI "pair programmers."

The synergy of AI agents, human intellect, and Cloud Development Environments (CDEs) is key. CDEs provide secure, governed, and scalable platforms for this collaboration, allowing developers to focus on business logic and innovation while AI handles the coding groundwork. This requires a move from rigid "gates" in development processes to flexible "guardrails" within CDEs. Such a move fosters innovation with built-in control and security.

Flexibility and choice are vital in this constantly advancing AI space. CDEs enable organisations to select the best AI agents for specific tasks, avoiding vendor lock-in by expressing the development environment as code. This leads to practical applications like faster prototyping, enhanced code development, and automated testing, significantly boosting code output. Furthermore, agentic AI democratises development, empowering non-engineers to build solutions.

Preparing for this future requires proactive experimentation through AI labs, engaging early adopters, and viewing AI as an augmentation of human skills. Watch the podcast for more insights on CDEs and the impact of AI agents on enterprise cloud development.

Agentic AI is a transformative technology for software development.

The role of developers is shifting from hard skills to soft skills.

AI agents can significantly increase productivity in coding tasks.

Organizations need to rethink their development strategies to integrate AI.

Cloud development environments are essential for safely using AI agents.

Choosing the right AI agent is crucial for effective development.

Security and governance are critical when integrating AI into development.

AI can empower non-developers to create applications.

Guardrails are more effective than gates in managing AI development.

Organisations should experiment with AI to find the best fit for their needs.

00:00 Introduction to Agentic AI and Developer Roles

03:20 Transformative Impact of AI on Development

06:50 Cultural Shifts in Development Teams

10:30 Integrating AI Agents in Cloud Development Environments

12:49 Choosing the Right AI Agents

15:21 Security and Governance in AI...