The rise of AI agents: A Look at the state of agentic AI in 2025
Artificial intelligence has been through plenty of “next big things.” A decade ago everyone was talking about deep learning; by 2023 we were drowning in large language models. Now, in the middle of 2025, the discourse has shifted again. This time the buzzword is agentic AI systems built from multiple models and tools that can plan, act and learn with minimal supervision. As one Deloitte forecast noted, about a quarter of companies already using generative AI expect to pilot agentic systems in 2025, rising to half by 2027. That sort of growth implies more than hype. To understand why so many technologists and executives are paying attention, let’s take a deeper look at what AI agents are, how they are being used today and where the field is heading.
From Chatbots to Agents
The idea of an “agent” isn’t new. Since the earliest days of AI, researchers have used the term to describe software that acts on behalf of a user. In the 1990s, simple reflex agents responded to environmental stimuli using hard‑coded rules, while goal‑based and utility‑based agents introduced planning and preference functions. What’s different today is the underlying technology: modern agents are built on top of large language models (LLMs) and can draw on external tools, memory and self‑improvement loops. They don’t just chat; they perceive, reason, plan and act, and they can improve through feedback.
That autonomy distinguishes AI agents from chatbots. A chatbot’s only job is to generate conversational responses. A good large language model can summarize documents or answer questions, but it remains a passive component. In contrast, an AI agent is designed to achieve goals in a complex environment. It will break a task into sub‑steps, select the right tools, execute those steps and update its own state based on the results. The MarkTechPost guide notes that agentic systems combine decision‑making, memory, multi‑step planning and tool execution. This extra “agency” means they can be left alone to complete tasks and only occasionally report back.
This distinction matters when we talk about generative AI. Many people use the term “agent” loosely, applying it to any AI software with a user interface. Deloitte cautions that chatbots and co‑pilots, though impressive, “lack the degree of agency and autonomy that agentic AI promises”. True agents choose their own actions to accomplish the user’s goal. They can plan and adjust the plan mid‑execution, and they work with a persistent internal state. Where a co‑pilot generates suggestions, an agent decides, acts and verifies.
Key Components of Modern AI Agents
To build such autonomy, developers assemble modules. MarkTechPost breaks down an agent into seven key components: perception, memory, planning, tool use, reasoning, feedback loops and a user interface. Perception covers the input interfaces text prompts, API calls, sensors that allow the agent to observe the world. Memory comprises both short‑term context and long‑term storage, often implemented with vector databases or relational stores. Planning and decision‑making modules use search algorithms, reinforcement learning or tree‑of‑thought approaches to map a route from the current state to a desired goal.
Tool use is what allows a model to go beyond language generation. Using function calling or external APIs, an agent can fetch data, perform computations, write files, send emails or invoke other services. In 2025, widely used frameworks like LangChain, AutoGen and Microsoft’s Semantic Kernel provide standardised interfaces for connecting to these tools. Reasoning and control logic tie the modules together; they evaluate the outputs of perception and memory modules, decide on the next action and route requests to the appropriate tool. Finally, feedback and learning loops allow agents to assess how well they are doing and adjust accordingly.
If this sounds familiar, that’s because the structure echoes classical AI textbooks. What’s new is the combination of LLMs for language understanding and synthesis with vector memory, search, planning and tool interfaces. This modularity has given rise to a cottage industry of frameworks aimed at reducing the friction of building and deploying agents.
Frameworks and Tools: The Agentic Ecosystem
By mid‑2025 there are dozens of platforms and libraries for building agents. MarkTechPost lists several, including LangChain, Microsoft AutoGen, Semantic Kernel, OpenAI Agents SDK, SuperAGI, CrewAI and IBM watsonx Orchestrate. Each has its own philosophy and strengths. LangChain dominates open‑source development and provides primitives for composing chains of prompts, memory stores and tool calls. It integrates with numerous LLM providers (OpenAI, Anthropic), vector stores (FAISS, Weaviate), and external APIs. AutoGen, by contrast, focuses on multi‑agent orchestration; it defines roles like Planner, Developer and Reviewer that can collaborate on code automation tasks.
Semantic Kernel offers an enterprise‑grade toolkit that abstracts LLMs behind “skills” and planners and is language‑agnostic. OpenAI’s Agents SDK (often referred to as Swarm) provides light‑weight constructs for defining agent behaviors, tools and guardrails with integrated monitoring. SuperAGI positions itself as an operating system for agents, offering persistent multi‑agent execution, memory handling and a marketplace for plug‑and‑play components. CrewAI emphasises team‑style orchestration by letting developers assemble specialised roles like Planner, Coder and Critic that communicate and hand off tasks. IBM’s Watsonx Orchestrate takes a no‑code approach: it is a SaaS platform that business users can drag‑and‑drop into workflows.
The ecosystem is evolving quickly. There is also an emerging taxonomy for multi‑agent architectures. InfoServices’ deep‑dive into LangChain’s multi‑agent system describes four core agent types: Planner, Executor, Communicator and Evaluator. The Planner decomposes a high‑level goal into subtasks and sequences them. Executors perform specific actions like retrieving documents, generating code or translating content. Communicators facilitate handoffs between agents and ensure context is preserved. Evaluators check outputs for correctness and may trigger retries or route tasks to different agents if needed. Above these agents sits an orchestration layer that can design graph‑based workflows, route tool calls and manage memory. Features such as dynamic tool routing, context sharing, asynchronous execution and error recovery flows make these multi‑agent systems robust and adaptable.
Use Cases and Real‑World Impact
What can agents actually do? Practical use cases span a remarkable range of industries. The MarkTechPost article highlights several categories. In enterprise IT, AI agents act as helpdesk assistants, triaging tickets and diagnosing issues. Tools like IBM’s AskIT reportedly reduce support calls by 70%. Customer support and sales agents integrate with CRMs and knowledge bases to handle common inquiries, manage returns and recommend products; some e‑commerce bots have cut support costs by 65% and increased lead volume by 50%. Contract and document analysis agents extract and summarise legal and financial documents, cutting review time by up to 75%.
In e‑commerce, agents monitor inventory, predict demand and help shoppers find products using visual search. Logistics companies like UPS save hundreds of millions of dollars annually by using AI route optimisation systems. Human resources and back‑office workflows are another fertile ground: digital HR agents handle routine queries, process payroll and manage invoices. Meanwhile, research agents summarise reports, fetch relevant insights and build dashboards, and AI assistants in developer tooling accelerate code generation and testing.
One high‑profile example is Devin, an autonomous software engineer introduced by Cognition in March 2024. Deloitte describes Devin as an agent capable of reasoning, planning and executing complex programming tasks. Devin turned natural language descriptions into working code, tested and fixed bugs and even trained models. Early benchmarks showed Devin could resolve about 14% of real‑world GitHub issues, outperforming standard LLM chatbots by a factor of two. While far from replacing human developers, such systems demonstrate the potential for agentic AI to automate multi‑step workflows.
Adoption, Hype and Reality
With new frameworks and successful pilots, it’s easy to get swept up in excitement. Deloitte’s 2025 prediction that 25% of gen‑AI‑using companies would experiment with agentic AI, and that this figure would double by 2027, underscores the momentum. Investors have poured more than $2 billion into agentic AI startups over the past two years. Companies are not only building new tools but also acquiring startups and licensing technology to accelerate their programs.
However, there are reasons to temper expectations. Traditional chatbots and co‑pilots already struggle with hallucinations and unpredictable behaviour. Agents layer planning and tool execution on top of those same language models. As Deloitte notes, the “autonomous” part of agentic AI may take time for wide adoption. In fields like software engineering, early agents like Devin show promise but still make too many mistakes to be trusted with complex tasks without human oversight. Agents must also cope with edge cases, ambiguous instructions and dynamic environments. As MarkTechPost points out, emergent benchmarks such as AARBench, AgentEval and HELM are being developed to measure how well agents handle tool use, long‑term memory and holistic task execution.
Another challenge lies in security and reliability. Agents that can call external APIs or execute code may inadvertently produce harmful outputs if not properly sandboxed. The MarkTechPost guide emphasises the need for improved planning algorithms, multi‑agent coordination, self‑correction mechanisms and secure tool sandboxes. Enterprises must also consider issues around data privacy, compliance and auditability when deploying agents that read contracts or access sensitive systems. Additionally, there is the human factor: knowledge workers may be sceptical about delegating tasks to autonomous systems, especially when mistakes could have significant consequences.
On the cultural side, agentic AI has sparked debate about the role of automation in knowledge work. Some view agents as productivity multipliers that free humans to focus on creativity and complex decision‑making. Others worry about job displacement and deskilling. The truth likely lies somewhere in between: as with previous waves of automation, agentic AI will change the nature of some jobs while creating new roles (for example, prompt engineers, agent orchestrators and AI ethicists). For now, most organisations treat agents as assistive tools rather than autonomous employees. Humans remain in the loop, setting goals, reviewing results and handling exceptions.
Building with Agents: Best Practices
If you are exploring agents as a developer or architect, there are a few principles worth keeping in mind:
Start with clear objectives and scope. Agents excel when their goals are well defined. Before integrating them into your workflow, articulate what success looks like and how you will measure it. Use existing benchmarks and metrics like task completion rate, cost per task and error rate to compare different designs.
Choose the right framework for your needs. LangChain is a good starting point if you want flexibility and a large ecosystem. AutoGen or CrewAI might be better if you need multi‑agent collaboration. For enterprise settings, Semantic Kernel or IBM Orchestrate offers stronger governance and integration options.
Design for observability and safety. Instrument your agents to collect logs, track tool calls and capture state transitions. Implement guardrails such as timeouts, cost limits and input sanitisation. Where possible, keep humans in the loop for critical decisions.
Invest in memory management. Decide what information should persist between sessions and how to store and retrieve it efficiently. Vector stores like FAISS or Weaviate are popular choices, but relational databases can be used for structured data.
Anticipate failure modes. Build mechanisms for error recovery retry strategies, fallback prompts and alternate agents as suggested by the InfoServices article. Evaluate your agents on edge cases and adversarial inputs.
The Road Ahead
Agentic AI is still in its infancy. Yet the trajectory is clear: as models improve, memory grows cheaper and planning algorithms become more sophisticated, agents will take on more complex tasks. MarkTechPost lists research directions like graph‑of‑thoughts planning, multi‑agent coordination, persistent memory, role guardrails and self‑correction strategies. We can already see these ideas manifest in frameworks like LangGraph, which uses directed graphs to model agent interactions, and evaluation suites like AgentBench and HELM.
Another interesting development is the move toward specialised agents. Rather than building a monolithic generalist, developers are creating swarms of narrow agents that collaborate. For example, a reading assistant might comprise one agent for summarisation, another for fact checking and a third for question answering. The InfoServices architecture emphasises decoupled agents with specialised roles. This modularity aligns with the microservices paradigm in software engineering and makes systems easier to scale and maintain.
Finally, the business impact will depend on how organisations manage the socio‑technical transition. Companies that successfully deploy agents will treat them as part of a broader digital transformation strategy rather than a magic bullet. They’ll invest in training, change management and robust governance. And they’ll recognise that agentic AI is not about replacing humans but about augmenting them freeing us from repetitive tasks so we can tackle the problems that still require creativity, empathy and domain expertise.
AI agents represent a compelling evolution in software design, combining the generative power of large language models with the autonomy of classical AI. They are not just glorified chatbots; they perceive, plan, act and learn. Frameworks like LangChain, AutoGen and Semantic Kernel make it easier than ever to build such systems, while multi‑agent architectures add robustness and scalability. The use cases are expanding from IT support and sales to legal analysis, logistics and software development. Early successes like Cognition’s Devin showcase both the promise and the limitations of today’s agents.
Yet the road to widespread adoption will be long. As Deloitte points out, only a fraction of companies will run pilots in 2025, and reliability, security and governance challenges remain. Researchers are working on better planning algorithms, evaluation benchmarks and self‑healing mechanisms. Meanwhile, business leaders must navigate the human factors of trust, transparency and job design.
For those building the next generation of intelligent systems, the advice is simple: stay grounded in real‑world needs, embrace modular design, invest in safety and never lose sight of the human at the centre. Agentic AI is an exciting frontier but like any frontier, it requires careful exploration.