Agentic artificial intelligence (AI) systems are fast-emerging as the next leap forward in AI systems development. Unlike traditional generative AI, which excels at creating content and responding to specific queries, agentic AI takes this capability further by enabling autonomous decision-making and proactive task execution within a given environment. This shift from passive content creation to active, decision-making AI has the potential to revolutionise how businesses operate. At a high level, while generative AI creates, agentic AI acts.
What is an AI agent?
An AI agent is a software program (Figure A) designed to interact with its environment, gather data, and perform tasks autonomously to achieve predefined goals. While humans define these goals, the AI agent independently determines the best course of action to fulfill them.
For instance, in a contact centre, an AI agent can resolve customer queries by asking questions, retrieving information from internal resources, and providing solutions. Based on the customer's responses, it decides whether to resolve the query itself or escalate it to a human representative.
AI agents function as rational agents that can analyse data and their environment to make informed, goal-oriented decisions. The following key capabilities set them apart from traditional software:
- Perception and Data Gathering: AI agents use interfaces to sense their environment. For instance, a robotic agent collects data through physical sensors, while a chatbot interprets text input from customer queries.
- Decision-Making: Using the collected data, AI agents predict the best outcomes and determine the next steps to achieve their objectives. For example, a self-driving car navigates obstacles using sensor inputs and pre-trained models.
- Collaboration: AI agents can collaborate with other agents and humans, in order to complete multi-step complex tasks.
Figure A: AI agent architecture
Source: Falkor DB
Benefits of AI
AI agents bring significant advantages to businesses and customers alike by streamlining operations and enhancing experiences. Key benefits include:
1. Enhanced Productivity
AI agents streamline operations by automating tasks, reducing inefficiencies, minimising human errors, and eliminating the need for manual effort, resulting in lower operational expenses. Their ability to adapt to evolving conditions ensures steady performance, further enhancing resource efficiency.
2. Cost Efficiency
AI agents automate workflows, cutting down inefficiencies, reducing human errors, and eliminating manual tasks, which lowers operational costs. Their flexibility allows them to maintain consistent performance, even in dynamic conditions, further enhancing resource utilisation.
3. Improved Decision-Making
Sophisticated AI agents utilise machine learning (ML) to process vast amounts of real-time data. This capability enables businesses to make accurate predictions and data-driven decisions swiftly. For example, AI agents can analyse market trends to optimise product positioning during an advertising campaign.
4. Superior Customer Experience
AI agents enhance customer engagement by delivering personalised interactions, prompt responses, and tailored recommendations. Businesses can boost customer satisfaction, conversion rates, and loyalty through these advanced, data-driven insights.
Given the unique capabilities of AI agents, the world’s transition to using active AI agents could prove to be an inflection point in how software is built and consumed. Currently, several major software businesses are evolving their technology stacks with an AI-first strategy.
For instance, enterprise giant Microsoft has integrated AI agents across its enterprise suite through its Copilot initiative, with capabilities ranging from automated coding, email summarisation, intelligent scheduling and improved search capabilities.
Similarly, IT workflow automation company ServiceNow is deploying specialised AI agents for IT service management, customer service, HR, and procurement workflows. These agents can handle everything from IT ticket resolution to employee onboarding, learning from each interaction to improve their performance while maintaining human oversight for critical decisions.
AI agents: A brief history
The concept of AI agents is not entirely new – it has roots in the “expert systems” of the 1960s and 1970s. These early attempts at AI used rule-based programming to mimic human decision-making in specific domains.
MYCIN, developed at Stanford in the 1970s, was one of the first expert systems. Its inference engine could diagnose blood infections and recommend antibiotics, using approximately 600 predefined rules. While groundbreaking for its time, these rule-based systems were inherently limited by their inability to learn from experience or handle novel situations.
Arguably the biggest step change enabling modern AI agents came with two more recent significant advances in artificial intelligence. First, deep learning breakthroughs in the 2010s enabled systems to learn from vast amounts of data and improve their performance over time. Then, the development of large language models (LLMs) in the early 2020s provided the sophisticated reasoning and natural language capabilities that make today's AI agents possible.
These foundation models dramatically improved natural language processing capabilities, enabling AI agents to understand nuanced instructions, generate contextually appropriate responses, and engage in more natural interactions. This advancement means agents can now interpret complex business requests and communicate their actions clearly to users.
Another crucial innovation is retrieval-augmented generation (RAG), which allows AI agents to combine their built-in knowledge with real-time access to enterprise data and documents. This technology enables agents to ground their responses in an organisation's specific context, policies, and procedures while maintaining accuracy and relevance. RAG is a key tool in preventing LLMs from “hallucinating” – that is, generating misleading or inaccurate results.
Overall, this progression from simple rule-based systems to today's sophisticated AI agents represents more than incremental improvement – it marks a fundamental shift in how organisations can deploy AI. Rather than following predetermined rules, modern agents can understand context, learn from experience, and autonomously pursue objectives while operating within appropriate business constraints.
AI agents in action
Major technology companies are making significant investments in agentic AI as the convergence of advanced language models, deep learning capabilities, and enterprise integration frameworks has created new opportunities for business transformation. In terms of the business case, there are opportunities with respect to cost efficiency, enhanced productivity, and improvements in outcomes, as demonstrated by a number of early deployments:-
Automating internal operations: At telecommunications giant Deutsche Telekom, an AI agent named “askT” is transforming the company’s internal operations. Serving approximately 10,000 employees weekly, askT not only answers questions about internal policies and benefits but is also actively managing administrative tasks like leave applications. In addition to cost efficiencies, the system provides consistent, 24/7 service availability while continuously learning from interactions to improve its responses.
Reimaging financial analysis: In the financial sector, organisations are leveraging AI agents' sophisticated analytical capabilities. Moody's, for instance, has implemented AI agents to analyze vast amounts of market data and company financials, enabling more comprehensive risk assessments. Moody’s system consists of a network of 35 specialized AI agents, each designed for specific analytical tasks and working within a coordinated multi-agent system. According to Nick Reed, Chief Product Officer at Moody's, these agents have transformed how the company conducts crucial research tasks that were previously outsourced to lower-cost regions, such as industry benchmarking and SEC filing reviews.
Customer experience innovation: eBay's implementation of AI agents is another illustration of the technology’s potential impact across multiple business functions. The company’s sophisticated “agent framework" orchestrates multiple LLMs for various tasks such as assisting with customer service inquiries, writing code, and creating marketing campaigns. The system learns from employee interactions to understand specific preferences and work styles, enabling increasingly autonomous operation. eBay is considering expanding this capability to help buyers find items and assist sellers with listings, showcasing how AI agents can enhance core business operations while improving the customer experience.
The SaaS disruption debate
Microsoft CEO Satya Nadella made a rather provocative claim during his appearance on the BG^2 podcast with hedge fund manager and venture capitalists Brad Gerstner and Bill Gurley. Nadella noted that "the business logic is all going to these AI agents" and suggested that they would fundamentally transform how organisations interact with their enterprise Software as a Service (SaaS) applications. This viewpoint challenges the traditional SaaS model that has dominated enterprise software for the past two decades.
Currently, business applications like CRM, ERP, HCM, and IT workflow systems primarily function as sophisticated data management tools, operating on a CRUD (Create, Read, Update, Delete) model with humans directing most operations. Nadella's vision suggests a fundamental shift where the intelligence layer of these systems will migrate from isolated applications to a unified AI layer in the technology stack.
In a utopian world with no accumulated technology debt, one could imagine a single AI layer seamlessly handling almost all business processes and functionalities. This scenario is not entirely theoretical; tech-savvy startups might build their systems around an AI-first architecture, potentially bypassing traditional business applications in favor of a more streamlined, AI-driven approach with simple database backends.
However, the reality is more complex. Enterprises have valuable data locked within various legacy systems and platforms, therefore realising a unified AI tier that can seamlessly orchestrate all business operations is extremely challenging. Furthermore, the incumbent leaders are not standing still. For instance, ServiceNow's AI agents are being designed to work across previously siloed workflows, from IT service management to HR processes. Similarly, Microsoft is integrating Copilot capabilities throughout its enterprise suite, while Salesforce's Einstein GPT and AI Cloud are reimagining customer relationship management and employee interactions through the lens of AI-driven automation.
Given the current state-of-play, predictions about the death of SaaS appear exaggerated and we expect many SaaS players to evolve by incorporating AI features. Companies that can effectively bridge the gap between legacy systems and AI-driven operations stand to capture significant value in the enterprise market. Infrastructure providers supporting AI operations, particularly those offering solutions for data integration and AI orchestration, could see increased demand. Additionally, new entrants might emerge with AI-first approaches that challenge traditional enterprise software categories entirely. The winners in this transition will likely be those who can effectively combine deep domain expertise with advanced AI capabilities while addressing the practical challenges of enterprise data integration.
Sizing the opportunity
The emergence of AI agents represents part of a broader technological transformation that analysts believe could rival the mobile and cloud computing revolutions in scope and impact. According to Battery Ventures' 2024 State of the OpenCloud report, the AI software TAM is worth ~US$4 trillion as it disrupts traditional software, services, and labour markets (Figure B).
Figure B: The Massive AI Market Opportunity
Source: Battery Ventures, State of the OpenCloud Nov 2024
The precise market sizing for agentic AI remains unclear due to the emergent nature of this technology. However, we note that the impact of AI agents extends beyond direct market size. Cloud providers are making unprecedented investments in AI infrastructure to support these emerging technologies. This infrastructure buildout provides the foundation for widespread adoption of AI agents across industries.
Going forward, it will be important to watch the pace of agentic AI technology adoption within enterprises as well as regulatory developments. While some regulations may stifle deployment, ultimately regulation developed in conjunction with industry leaders and other key stakeholders will likely create a more stable environment for long-term investment.
Conclusion
Agentic AI is a fundamental shift in enterprise technology moving beyond simple automation to create systems capable of autonomous, complex decision-making. While early adoption shows promising results, success will depend on several critical factors.
For investors evaluating opportunities in this space, key considerations include:
- The pace of enterprise adoption and deployment success rates
- Regulatory developments, particularly around autonomous decision-making
- Competition between incumbent SaaS providers and AI-first startups
- Infrastructure requirements and associated investment opportunities
The winners in this transition will likely be companies that can effectively bridge the gap between legacy systems and AI-driven operations, while addressing the practical challenges of enterprise data integration. We believe both established players as well as new AI-first players can win in this market. The opportunity also extends beyond direct AI agent providers to the broader ecosystem of companies providing essential infrastructure, security, and integration services. For investors, agentic AI represents a potentially transformative opportunity – but one that requires careful analysis of technological capabilities, business models, and go-to-market strategies.
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