In this article, New Relic’s Head of AI and ML Innovation, Camden Swita, discusses the evolving role of AI in software development. As AI becomes more capable, it’s essential for engineers to understand its potential beyond simple tasks. Swita emphasizes measuring AI’s success through time saved rather than just usage stats. Key to effective AI implementation is setting clear goals, collaborating with domain experts, and ensuring data accessibility. New Relic is creating specialized AI agents to improve efficiency and accuracy while reducing repetitive tasks. Despite initial skepticism, Swita believes the adoption of AI will grow rapidly as more engineers recognize its benefits.
Don’t Miss Out on AI Innovations in Engineering
In today’s rapidly evolving tech landscape, engineering leaders are under pressure to embrace AI while addressing challenges related to its adoption. Camden Swita, the Head of AI and ML Innovation at New Relic, emphasizes that successful integration of AI isn’t just about the technology itself—it’s also about managing the human side of the equation.
AI is transitioning from simple tools to agentic software capable of automating complex tasks. By integrating advanced language models with real-world tools, New Relic is witnessing engineers becoming more engaged with this new technology. "We must measure success in terms of time saved rather than just usage metrics," states Swita. This shift in focus allows teams to achieve more significant efficiency gains.
Highlights of AI Agent Implementation:
- Time Savings: AI agents can drastically reduce task completion times. For example, generating GraphQL queries can go from two and a half hours to just thirty minutes.
- Goal Setting: Defining clear objectives for AI agents helps improve accuracy and effectiveness in various processes, such as reducing mean-time-to-resolution and automating repetitive workflows.
- Data Usage: New Relic is experimenting with different data types, finding that sometimes image-to-text models yield better results than traditional text-based models.
- Collaborative Approach: Involving domain experts is vital. Engaging stakeholders who design the tools can foster better understanding and trust in AI.
As AI agent technology continues to develop, Swita believes that adopting a multi-agent approach—where specialized agents handle specific tasks—will prove beneficial. He encourages companies to focus on improving access to structured data and knowledge graphs, enabling seamless AI integrations within organizational processes.
In summary, embracing AI in engineering requires a balanced approach that prioritizes both innovation and collaboration. As these technologies mature, organizations are bound to experience significant changes in productivity and efficiency.
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Tags: AI in Engineering, Automation, Productivity, Tech Trends, Engineering Leadership, New Relic
What are AI agents in DevOps?
AI agents in DevOps are smart tools that help teams automate tasks and make better decisions. They analyze data and provide insights to improve software development processes and overall project efficiency.
How do AI agents improve DevOps?
AI agents improve DevOps by speeding up workflows, reducing errors, and enhancing communication among team members. They can predict issues before they happen and suggest solutions, making the development process smoother.
What is the missing link in DevOps?
The missing link in DevOps often refers to the integration of AI and machine learning. While DevOps focuses on collaboration and automation, adding AI technologies can enhance decision-making and problem-solving abilities, ultimately leading to faster and more reliable software delivery.
Can AI agents work with existing DevOps tools?
Yes, AI agents can easily integrate with existing DevOps tools. They can analyze data from these tools and provide insights that help teams optimize their workflows without needing to change their current systems.
Are AI agents expensive to implement in DevOps?
Implementing AI agents can vary in cost. While some solutions may require investment, the long-term benefits, like increased efficiency and reduced errors, often outweigh the initial costs. Many teams find that the savings from improved processes justify the expense.
