AI Agents are Everywhere, But No One Knows Why
Software framework LangChain recently published a report surveying over 1,300 professionals, to “learn about the state of AI agents” in 2024. While 51% of the respondents said they have already been using AI agents in production, 63% of mid-sized companies deployed agents in production, and 78% have active plans to integrate AI agents. Furthermore, the survey also revealed that professionals in non-technical companies are also willing to deploy AI agents. It stated, “90% of respondents working in non-tech companies have or are planning to put agents in production (nearly equivalent to tech companies, at 89%).” Even Research and Market’s report on ‘AI Agents Market Analysis’ indicates an optimistic future for AI agents. “The AI agents market is projected to grow from $5.1 billion in 2024 to $47.1 billion in 2030, with a CAGR of 44.8% during 2024-2030,” read the report. The numbers indicate a resounding shift in sentiment towards AI agents, moving away from the prohibitive scepticism. Agent or Assistant? While a majority of the respondents in Langchain’s survey revealed using AI agents for research summarization and personal assistance, a notable 35%  said they use them for coding tasks. Companies, however, haven’t settled on a definition for AI agents yet. A spectrum, or absolute autonomy? Earlier, Google stated that 25% of all newly written code is AI-generated, a revelation that sparked criticism. For instance, a user on HackerNews suggested Google’s claim might be overstated, arguing that it primarily relies on a code-completion engine. Meanwhile, a Reddit user observed that Google was indicating “clean-up jobs for dependencies, removing deprecated classes, or changing deployment configurations”. A few days ago, payment processing giant Stripe launched a software development kit (SDK) for AI agents. This SDK allowed LLMs to call functions related to payments, billing, and issuing, enabling agents to ‘spend’ funds and accept or decline payment authorisations. Several users on X questioned the reality of the feature and asked if it was just a fancier way to refer to API and function calls. “I mean, for me, at least, this just removes ten lines of code and proposes a more complex pricing model. At the end of the day, am I missing something?” said a user on X. At Oracle CloudWorld in 2024, the company announced over 50 AI agents in the Fusion Cloud Application suite. Oracle’s executive vice president of applications development, Steve Miranda, however, was quite transparent about the definition of an agent. During an interaction with AIM, he said, “I think that early use cases will be a little bit less completely autonomous and more human-assisted.” Similarly, Ketan Karkhanis, CEO of ThoughtSpot, while talking to AIM, explained that many systems today, such as Microsoft’s Copilot, operate on single-turn Q&A, answering one question at a time. They lack reasoning, adaptability, and the ability to learn a user’s business to be called autonomous. “There are a lot of nuances to this. If you can’t coach it, then it’s not an agent. I don’t think you can coach a copilot. You can write custom prompts [but] that’s not coaching,” Karkhanis added. Even Salesforce CEO Marc Benioff has often criticised Microsoft’s approach towards AI agents and accused them of falsely marketing Copilot’s capabilities. While there isn’t a universally accepted definition yet, companies are claiming an improvement in several operations with the use of AI agents. As Good as Agent 007 The survey received criticism on social media. A user on X posted, “In this day and age, surveys are the worst indication of real usage. Show us actual real usage tracking metrics that you can collect.” Despite their skewed definitions, several organisations, including some of the biggest names, are achieving success with AI agents. A few weeks ago, Freshworks unveiled a new version of Freddy AI, an autonomous agent that resolved 45% of customer support requests and 40% of IT services (on beta). Even Salesforce announced the availability of Agentforce, which enabled their customers to deploy AI agents on their platform. One of Salesforce’s customers, publishing company Wiley, reported a notable success with Agentforce. “With the help of AI productivity tools, Wiley was able to onboard seasonal agents 50% faster, leading to a 213% return on investment and $230,000 in savings,” said Wiley wrote a blog post. Wiley also mentioned that Agentforce showed a 40% improvement in customer case resolution compared to their previous chatbot. This is in line with LangChain’s survey, in which 45.8% of the participants mentioned deploying AI agents in customer support and service. Salesforce continues to remain bullish over an agentic future. “In 2025, we’ll increasingly see more complex, multi-agent orchestrations solving higher-order challenges across the enterprise, like simulating new product launches or marketing campaigns and developing recommendations for adjustments,” said Mick Costigan, VP of Salesforce Futures. Moreover, companies who have actively deployed AI agents, continue to improve accuracy and reduce operational costs. Amdocs, a telecommunications company, built AI agents using NVIDIA’s NIM Microservices, which increased AI accuracy by 30%. Further, Amdocs reported a notable decrease in operational costs, by reducing token usage by 60% for data preprocessing and by up to 40% for inferencing. The Human Touch Contrary to the popular definition of AI agents operating autonomously, there’s a good reason why they aren’t. In LangChain’s survey, a majority of respondents expressed the need for ‘tracing and observability’ to oversee autonomous operations. More than 35% of companies prioritise online or offline evaluation of the agents’ output results. Most of the surveyed companies granted agents read-only permissions, and very few, around 10% of the companies, granted agents full read, write, and delete permissions. Even if risks and concerns are alleviated, AI agents may not fully understand the nuances of each aspect of the operation. During a conversation with AIM, Sam Mantle, CEO of Lingaro Group, stressed the importance of handling the flow of data between each individual component in an operation, which is often disconnected. “I’m interested in [knowing] who owns the data component that may sit in that application, because if we really want to streamline things, somebody has to be responsible for that data, no matter where it flows within the organisation,” Mantle further said.
Companies are going all in on AI agents, but are they skewing the definition of one?
Companies are going all in on AI agents, but are they skewing the definition of one?
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["AI", "ML", "LangChain", "agent orchestration", "Aim", "microservices", "TPU", "R", "Go", "API"]
Supreeth is an engineering graduate who is curious about the world of artificial intelligence and loves to write stories on how it is solving problems and shaping the future of humanity.