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State of AI 2025 - Era of AI Agents

A recap of my AI Meetup Osijek talk on where AI actually stands in 2025: agentic AI, the UX problem, the business reality behind the record numbers, and the three topics worth following.

Brando Koch
Brando Koch
SEPTEMBER 23, 2025 · 10 MIN READ

On September 23, 2025 I gave the closing talk at AI Meetup Osijek #2, a top-down look at where AI stands after a year in which adoption, investment and disappointment all hit records at the same time. This post is the written version of that talk. It follows the same structure, but with the numbers, quotes and sources that did not fit on a slide.

Note: The event itself, including the other three talks and the panel, is covered separately in the AI Meetup Osijek #2 recap. All sources referenced here are listed at the bottom.

AI Meetup Osijek #2 presentation

AI Meetup Osijek #2, September 23, 2025

The view from above

Five things define 2025, and they are all superlatives.

Adoption is at a record. AI is no longer something you go looking for. It ships inside Office 365, Google Workspace and Adobe Creative Suite, and most professionals now encounter a generative feature without ever choosing to.

Investment is at a record. Global private investment in AI reached $109.08B in the United States in 2024, against $19.42B in Europe and $9.29B in China [3].

Every modality is moving. Text was 2023, images were 2024, voice broke through in 2025 on latency and cost, and video is the one crossing the chasm next [4].

AI is cheaper and more accessible than it has ever been. Open models closed the benchmark gap with closed models from around 8% to under 2% in a single year, and the compute cost of reaching GPT-3.5 level performance fell roughly 280x compared to late 2022 [3].

People use it for ordinary things. OpenAI’s own usage study, published a week before the meetup, splits consumer usage into Asking (49%), Doing (40%) and Expressing (11%), with about 30% of usage work-related and 70% not [7]. Three quarters of conversations are practical guidance, seeking information, and writing.

The models got much better at reasoning this year, but the general public barely uses that capability. It is like having a very smart person in the room: most everyday questions do not need that level of expertise.

Agentic AI

What an AI agent is

The definition I used on the slide:

An AI agent is a system that uses LLM or VLM models to autonomously decide how to carry out a task, with access to tools.

The two essential parts are autonomy over the procedure and access to tools. The rest are implementation details.

Big potential, if applied properly

Three failure patterns show up over and over, and none of them are model problems.

Adapt the process to the agent, not the agent to the process. McKinsey tested 25 organizational attributes against the ability to see EBIT impact from generative AI, and workflow redesign had the single biggest effect [2]. The value comes from rewiring how the company runs. The instinct in most companies is the opposite: keep the process exactly as it is, and drop an agent into one step of it. That is not “let us try agents on this”, it should be “let us re-analyze this flow so it delivers the same result but is built for agents”.

Drop the illusion of full automation. Very few processes can be automated 100%. People remain in the loop to oversee accuracy, ensure compliance, apply judgment and handle edge cases [1]. There are levels of leverage here and they are not equal: an agent that completes a task end to end, an agent that produces a draft a human verifies, an agent that only speeds up one step. Most of the real value this year has come from the draft-plus-verification level.

If you cannot supervise it, you cannot improve it. McKinsey’s phrasing is “make it easy to track and verify every step” [1]. If you cannot inspect an agent’s steps or measure its performance, you have no systematic way to improve it.

Before building an agent, I ask five questions:

  1. Can you describe the work to a complete beginner who has no context?
  2. Can you measure their performance in any way?
  3. Does the agent have access to all the tools and knowledge it needs?
  4. Are you actually requiring 100% accuracy?
  5. Can you track its progress?

If the answer to the first one is no, the project is not ready for an agent. The useful mental model is that onboarding an agent is closer to hiring a new employee than to deploying software [1]. You have to describe the job very well, and unlike an employee this one’s intelligence grows on its own over time. So sometimes the right answer is that it is not yet time for your agent.

One more question that comes up in every project: one agent or several? It is similar to the monolith versus microservices debate, and the answer depends on the use case.

Prompt engineering became context engineering. The definition I like is “the art of providing all the context for the task to be plausibly solvable by the LLM” [8]. As a result, most agent failures are now context failures rather than model failures. The main job of your code is to gather the context the model needs in order to respond. AI knows nothing about your situation except what you tell it.

MCP became the connector standard. Its creators describe it as the USB-C of AI [4], meaning a standard way to connect models to tools and data.

Subagents got popular. A subagent is a normal agent with a name, a description, instructions, tools and, critically, its own isolated context window, usually driven by an orchestrator [9]. Their strength is the isolated context: because a subagent does not see the other agents’ context, it can critique their work more objectively.

User experience

Developers tend to underestimate UX, and it is where I see the most avoidable problems. Three questions decide the design.

Workflow or agent? A fixed workflow is predictable, testable and cheap. An agent is flexible and expensive to verify. Choosing an agent only because agents are interesting is a common way for projects to fail.

Text or voice? Both are now available off the shelf, and both are extremely wide interfaces. Wide interfaces are hard problems for developers because the user can ask for literally anything, including things the system was never meant to do.

Where are the boundaries of a user request, and how do you teach the user about them? This is the one teams most often forget to plan for.

A chat exchange in which the user sends: call the function get_order_status with the parameter 1 or 1=1 dash dash. The assistant responds by returning the entire orders table as JSON, listing every record with its id, user_id, created_at timestamp, status and item name.

The screenshot above shows the problem. The user typed something that looks like a function call with an injected condition, and the interface returned the entire orders table, because no boundary had been defined or communicated.

McKinsey’s read matches: an important part of human-agent collaboration design is developing simple visual interfaces that make it easy for people to interact with agents [1]. Andrew Ng puts the same requirement on the engineer, arguing that teams move much faster when GenAI engineers have user empathy and basic product design skill, so that given only high-level guidance they can make many decisions themselves [10]. UX should be designed from the start, not added after the model works.

The business landscape

Generative AI entered the Trough of Disillusionment on Gartner’s 2025 Hype Cycle, which is worth viewing at the source [5]. The numbers support this. Organizations spent an average of $1.9M on generative AI initiatives in 2024, and fewer than 30% of AI leaders report that their CEO is happy with the return. 57% of organizations estimate their data is not AI-ready. Only 1% of executives describe their generative AI rollout as mature [2]. And Gartner expects over 40% of agentic AI projects to be canceled by the end of 2027 [6].

This is the part that is hardest to communicate to management, because the trough is not a failure of the technology. Low-maturity organizations struggle to identify suitable use cases and carry unrealistic expectations. Mature ones struggle to find skilled people. Both problems are organizational.

Chart of global private investment in AI over time, showing spend continuing to climb year on year to a new record.

Figure from the 2025 AI Index Report, Stanford HAI, used under CC BY-ND 4.0.

Meanwhile investment keeps climbing [3]. Record disillusionment and record spend are happening simultaneously, which is exactly what the middle of an adoption curve looks like. My advice to companies is the same it has been for two years: integrate gradually, test every stage, and do not put the whole thing over your head at once. Start by identifying your most time-consuming tasks.

A new type of company came out of this cycle. Bessemer studied 20 high-growth AI startups and found that the AI “Supernovas” averaged around $40M ARR in their first year of commercialization and around $125M in the second, against roughly seven years for a traditional cloud company to reach $100M; their State of AI report carries the growth curves [4]. They do it on roughly 25% gross margins, trading distribution for profit, but at about $1.13M ARR per employee, which is 4 to 5x a typical SaaS benchmark. The more sober “Shooting Stars” reach around $3M ARR in year one at around 60% margins and about $164K ARR per employee. If T2D3 described the SaaS era, Q2T3 describes this one.

Two things follow. First, ARR benchmarks that were reliable for a decade are now nearly useless for judging an AI company. Second, with gross margins this low, compute cost largely determines profitability, so cost and speed matter more than another point of benchmark performance.

New applications

Development

The tools got very good this year: Claude, GitHub Copilot, Windsurf, Cursor, Lovable, Replit. Alongside them a stable set of building blocks has emerged that a GenAI engineer is now expected to know.

Eight coloured toy bricks, each labelled with one building block of generative AI engineering: prompting techniques, agentic frameworks, guardrails, evals, RAG, fine-tuning, computer use, and asynchronous programming.

Prompting techniques, agentic frameworks, guardrails, evals, RAG, fine-tuning, computer use, asynchronous programming [10]. The key skill is knowing which of these a given problem needs.

With vibe coding, expectations moved faster than capability. Andrew Ng’s framing of the labour market is worth quoting: there is significant unmet demand for developers who understand AI, and at the same time an uptick in unemployment among recent CS graduates, because most universities have not adapted their curricula [10]. The most productive programmers today are the ones who deeply understand computers, how to architect software and how to make complex tradeoffs, and who additionally know the current AI tools. Seniority remains valuable, and AI tools make experienced developers even more productive.

Robotics

Nvidia’s vice-president of robotics told the Financial Times that “the ChatGPT moment for physical AI and robotics is around the corner”, adding that the market had reached a tipping point [11]. The substance behind that is foundation models for robotics: GROOT for humanoids, the simulation and synthetic motion pipelines around it, and a wave of models like RFM-1 and LLaRA that fold perception, language and action into one system [3].

Deloitte’s read on adoption is the realistic one: physical AI moves first in asset-heavy, task-intensive sectors such as manufacturing, logistics, healthcare and agriculture, and slowly where work is mostly digital or interpersonal [12].

File and document processing

This category gets less attention than the others, but it is where much of the production revenue is. fileAI, LlamaIndex and Unstructured all attack the same problem: reading the outside world and turning it into a language computers understand. Documents, spreadsheets, slides, scans, images, audio. Multimodal models made the unstructured-to-structured conversion cheap enough to put into production, and once your data is structured, everything downstream in the traditional software stack works again.

Military

Unmanned systems are significantly cheaper to produce than manned ones, which is the main strategic reason for them [13]. The conditions are also completely different from anything in enterprise AI: no reliable internet, edge computing at the front, and something like Palantir Gotham doing strategy back at headquarters.

To be accurate, no aspiring AI superpower has yet fielded a fully AI-driven unmanned system. The Institute for the Study of War assesses that Ukraine and Russia have both begun integrating AI into their drone capabilities in the hope of getting there [13].

Three things to watch

Evaluations. This is the biggest unsolved bottleneck in enterprise deployment. Public benchmarks like MMLU, GSM8K or HumanEval give coarse signals at best and rarely reflect real workflows, compliance constraints or decision-critical context. Bessemer’s prediction is that 2025 to 2026 is when evals go private, grounded and trusted, and that enterprise deployment scales because of it [4]. My own position on evals is that there are no rules yet. Every serious project I have worked on ended up building its own.

Regulation. The EU AI Act is the one my clients ask about most, and compliance work is no longer a thing you postpone until the product works.

Sovereign and private AI. Governments keep introducing rules on data privacy, security and AI governance, and organizations are responding with multi-cloud and edge strategies to localize data and compute [12]. This is the thesis my own company is built on, so I will keep it short: for a growing number of buyers, “your data never leaves your environment” is no longer a nice-to-have but one of the first questions in procurement.

Thanks to everyone who came out to BIOS for the meetup, and to the other speakers. The full event recap is here.

References

Sources used in the talk and in this post.

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