Unlocking the Future of Software Development: The Transformative Power of AI Agents
AI agents are changing the game in software development. They go beyond a mere tool, reshaping how software is built and used across diverse industries. By automating workflows and handling tasks independently, AI agents boost efficiency and shape how new products are built.
Meanwhile, generative AI and various AI agent frameworks drive these agents, paving the way for rapid innovation.
In a recent “Chain of Thought” podcast episode, Conor Bronsdon, Head of Developer Awareness at Galileo and host, Yash Sheth, COO and co-founder of Galileo, and Atindriyo Sanyal CTO and co-founder of Galileo discussed all things AI agents and their evolution.
The Rise of AI Agents in Industry Automation
AI agents began in software development, but as Sheth notes, “Automation is where the real ROI is going to come from,” now reshaping finance, telecommunications, and regulatory defense with unprecedented efficiency.
In finance, AI agents handle complex tasks like processing transactions and updating old code from languages such as COBOL to newer ones. That approach not only speeds things up but also cuts down on mistakes.
The telecommunications sector uses AI agents to manage customer service and maintain infrastructure, making operations smoother and improving service quality. In regulatory defense, AI reviews documents and ensures compliance, tasks that used to take a lot of careful, time-consuming human effort.
Impact on Workforce and ROI
By taking over routine and labor-intensive tasks, AI agents free up the workforce to focus on more creative and strategic work. Tens of thousands of people processing transactions, documents in various verticals will gain new skills as manual work decreases.
The return on investment from using AI agents goes beyond saving money on labor; it also means higher productivity and the ability to grow without needing a proportional increase in staff, enabling scaling generative AI across enterprises.
As we move toward a future where every piece of software might include AI, businesses need to embrace these technologies to stay competitive. It’s no mere trend; it’s a necessary step for ongoing growth and innovation.
Evaluating AI Agents: The Need for Advanced Tooling
Adopting AI agents brings many benefits, but making sure they work well and reliably requires advanced evaluation tools. Sanyal makes this clear: “Robust evaluation tooling is not just a luxury but a necessity in our journey towards integrating AI into every facet of business operations.”
Understanding the right metrics for evaluating AI agents is critical to this process.
Challenges in Evaluating AI Agents
As AI agents take on more complex tasks beyond just generating text, we need better ways to assess what they’re doing. Sanyal points out that current tools are like “caveman tools,” inadequate for the job.
The challenge is to move beyond simple observations and ensure AI systems are evaluated with the thoroughness their complex tasks require, utilizing advanced LLM evaluation techniques.
Without proper evaluation, we risk encountering common AI agent failures that can undermine their effectiveness.
AI operates in diverse environments, so one-size-fits-all metrics don’t cut it. Understanding effective AI evaluation methods is essential. Sanyal suggests an “inversion of control,” giving developers the flexibility to create evaluations that fit their needs. Real-time evaluation is also crucial.
As AI agents perform actions, mistakes can have serious consequences, so assessments must adapt on the fly as application patterns and data evolve.
Preventing Errors and Unwanted Actions
Mistakes by AI agents, such as LLM hallucinations, can lead to serious, irreversible issues. That’s why a thorough evaluation process is more than just measuring performance—it’s about preventing costly errors before they happen.
Galileo’s approach includes a strong system for real-time detection and prevention of “bad behavior” by AI agents. That proactive approach ensures that as AI becomes more widespread, its operations stay safe, secure, and aligned with their intended purposes.
In short, developing advanced evaluation tools tailored to the unique challenges of AI agents is crucial. As more industries rely on AI solutions, having accurate, flexible, and real-time evaluation frameworks will be key to sustainable success.
Wider Industry Adoption and Productionization
Looking ahead, AI agents have huge potential for broader industry adoption. Automation is the main driver in 2025. And next year will see AI moving toward both product-market fit and product-tool stack fit.
“We’ll start seeing a lot of the engineering and systems around the LLM starting to mature,” Sanyal explains. This maturity will allow businesses to take early AI prototypes and turn them into fully functional solutions, broadening their use across different sectors.
Reducing Generation Latency
One current challenge for AI agents is generation latency. However, improvements are on the way. Cutting down the time it takes for AI agents to generate responses is essential, especially as they take on more action-oriented tasks beyond just creating text.
Advancements like the “Gemini 2.0 flash model” and innovative RAG system tools are proof of progress in this area. By reducing latency, AI agents can handle data processing, execute API calls, and manage multimodal inputs more efficiently, making them truly autonomous and useful in real-world applications.
Enhancing Multimodal Functionality
Improving the multimodal capabilities of Language Learning Models (LLMs) is another exciting area. Multimodality will become more significant, allowing AI agents to not only take language as input but also images and audio, aligning with current generative AI trends.
That approach will lead to hybrid systems that achieve user goals through a mix of different input types. Using multimodal data, AI agents can offer more comprehensive insights and solutions, making them invaluable across various industries.
Collectively, such advancements in reducing generation latency and enhancing multimodal functionality set the stage for stronger and more versatile AI agents. As these technologies advance, the opportunities to automate workflows and boost productivity across industries will grow, making AI agents a key part of the next wave of technological progress.
Embracing AI Agents: Preparing for the Future
Looking ahead, AI agents are set to become essential to business operations, fundamentally changing workflows and how value is created. The way forward is clear: success depends on thorough evaluation and flexibility. With automation advancing rapidly by 2025, AI will do more than help—it will actively shape decision-making and outcomes across industries.
Galileo is leading this transformation, offering the necessary tools for this evolution through our Evaluate, Observe, and Protect modules.
With Galileo’s innovative solutions and strong partnerships with industry leaders like HP and Twilio, companies will be well-equipped to use AI to unlock new potentials, ensuring security, compliance, and performance in their AI-driven futures.
For more insights into all things AI and evaluation from leading industry experts and observers, check out the Chain of Thought podcast.