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The era of AI agents is upon us—or is it?
When OpenAI released ChatGPT in November 2022, it wasn’t just a technological milestone; it was a spark that ignited our collective imagination. The extraordinary capabilities of large language models (LLMs) suddenly felt within reach of everyday applications. As these models become faster, more accurate, and increasingly affordable - the recent DeepSeek announcement here being just an example of this trend - the possibilities ahead seem boundless - bordering on science fiction.
Fast forward to 2025, and the conversation has shifted. Today, the buzz isn’t just about LLMs themselves but AI agents - autonomous entities that combine the natural language and reasoning prowess of LLMs with the ability to act on their insights. These agents are no longer just idea generators; they are doers. According to AI Agents Directory, a staggering 191 new AI agents launched in January 2025 alone, bringing the total to 866 across 45 categories. This rapid pace of innovation is nothing short of breathtaking, with no signs of slowing.
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The Rise of AI Agents for Software Development
Among the burgeoning categories of AI agents, one stands out: software development and coding. A recent headline in The Information captured a lot of attention on social media: OpenAI Targets AGI with System That Thinks Like a Pro Engineer. The ensuing Reddit discussion here on this article is particularly enlightening. Is this bold claim grounded in reality or fueled by hype?
At CurieTech AI, we’re also building coding agents, but with a distinct focus. While much of the media and investment focus is on tools for product engineers in software development, an entire segment of the developer community remains underserved: IT engineers.
The Overlooked World of IT Engineering
When we think of software development, it’s easy to default to the “Silicon Valley” archetype: teams crafting sleek apps and scalable platforms. However, this narrative overlooks the unsung heroes of digital transformation—the enterprise IT engineers. These professionals work behind the scenes to integrate systems, automate processes, and power the digital backbone of businesses across industries.
Integration is the lifeblood of IT engineering, and it’s no small feat. The 2024 Connectivity Benchmark Report reveals that a typical IT department juggles over 1,000 systems - spanning CRM, ERP, HRM, accounting, and more. To run and automate enterprise processes, these systems must share data seamlessly. Yet each system brings its own unique APIs, schemas, configurations, programming languages, and security protocols.
At the center of this complexity are integration engineers and architects, who are often under immense pressure to deliver with limited resources and tight deadlines. At CurieTech AI, we aim to ease their burden by creating AI agents tailored for their unique challenges - coding, testing, documentation, and troubleshooting - across the platforms and paradigms they already use. Our mission? To help IT engineers achieve 10x to 100x productivity gains.
The Accuracy Imperative
Achieving this level of acceleration hinges on one key factor: accuracy. The closer an AI agent’s output (code, tests, documentation) is to the developer’s intent, the fewer corrections and iterations are needed to reach a finished product.
Unfortunately, most generic coding agents have fallen short of their promise when it comes to accuracy. Another reddit thread paints the picture of what works and what doesn’t. Tools like GitHub Copilot and Cursor excel at small, localized tasks but struggle with complex, end-to-end project requirements. For IT integration tasks, the challenges are even greater. Many enterprise platforms - such as MuleSoft, Oracle Integration Cloud, SAP Integration Suite etc. - use proprietary languages like XML or DataWeave, which are underrepresented in the training data of generic LLMs. As a result, these agents lack the precision needed for real-world enterprise integration.
At CurieTech AI, we’ve taken a different approach. Instead of trying to “boil the ocean” like many generic tools, we’ve focused on building highly specialized agents for integration developers. By honing in on platforms like MuleSoft, we’ve achieved significantly higher accuracy and, in turn, greater productivity gains for our users.
For example, in a simple task to create DataWeave from input and output examples, CurieTech AI outperformed GitHub Copilot in both accuracy and speed. Watch the comparison here. If you’re curious, you can try this task for yourself by signing up here.
A Vision for the Future
While MuleSoft has been our starting point, our ambitions extend far beyond a single platform. The techniques we’ve developed to improve AI agent accuracy are applicable across other enterprise integration platforms—be it legacy systems like TIBCO and IBM ACE or modern leaders like Dell Boomi, SAP, and Oracle.
The integration landscape is also evolving rapidly, with new platforms built on microservices and cloud-native architectures emerging. No matter the platform, our focus remains the same: empowering IT engineers to deliver faster and with higher quality.
Enterprise integration is the engine that drives digital transformation, and IT engineers are the unsung heroes keeping that engine running. At CurieTech AI, we’re building tools to ensure these heroes have the support they need to thrive in an increasingly complex digital world.
Stay tuned as we continue this journey. Sign up for our newsletter to follow our progress and learn how we’re redefining what’s possible for IT engineering.
To bring this vision to fruition, we are fine-tuning our models, building retrieval augmentation and employing the most cutting-edge techniques coming out of research on how to increase the accuracy of agents. If you would like to join our team and join us in this journey, please check out our careers page.
Stay tuned as we continue this journey. Sign up for our newsletter to follow our progress and learn how we’re redefining what’s possible for IT engineering.
