The State of AI Agents 2025: A CEO’s framework to read the agentic market

1. From apps to Agents: The shift underway

The world’s tech giants are converging on a new paradigm: moving beyond user-driven software applications toward goal-driven AI agents that can autonomously execute entire workflows. This is not just a new product category, but a fundamental change in software architecture. In the past, humans navigated menus and dashboards; now AI agents take high-level goals and carry out the steps. Bain & Company predicts that within a few years, routine digital tasks will shift from “human plus app” to “AI agent plus API,” underscoring this transformation. Microsoft has explicitly declared “We’ve entered the era of AI agents” , noting that over 230,000 organizations (including 90% of the Fortune 500) have already used its Copilot platform to build AI agents automating work . For CEOs, the message is clear: the software paradigm is evolving from apps that users operate to agents that achieve users’ goals.

2. The competitive landscape: five archetypes

The emerging agentic AI market is fragmenting into several archetypes. Each represents a different strategy for delivering AI agent capabilities:

CEO takeaway: The AI agent landscape ranges from closed, vertically-specialized systems to open frameworks, and from plug-and-play copilots to bespoke enterprise platforms. In evaluating this landscape, a chief executive should note whether a solution is a sealed black-box agent (with tight control but less flexibility) or an open, extensible framework (more flexibility but requiring more development). The market is also bifurcating between vertical agents tailored to specific industries and horizontal platforms that can be adapted across use cases. Understanding these archetypes will help leaders map the competitive terrain and identify where their organization fits or can differentiate.

Read our previous article to know more about what's missing in agent architecture to reach AI reliability in Enterprise

3. Emerging architecture: eight building blocks of the Agent stack

As the agentic ecosystem matures, a consensus is forming around eight core layers that together constitute a complete AI agent stack (from low-level infrastructure to high-level oversight). Forward-looking teams are increasingly designing solutions with each of these layers in mind:

1. Infrastructure Layer: The compute foundation for agents. This includes cloud or on-premise servers, GPUs/TPUs, and orchestration tools that keep agents running reliably . (In essence, the “boring” plumbing of cloud, storage, and networking that is often taken for granted but crucial for scale and uptime.)
   
   
   

2. Data & Semantic Layer: Handles knowledge storage and context for agents. Vector databases, knowledge graphs, and memory caches live here, allowing an agent to remember facts and conversations. This layer semantically indexes data so that agents can retrieve relevant information on the fly (the source of long-term context and personalization – increasingly a key differentiator ).
   
   
   

3. Integration Layer: Connectors and APIs linking agents to external systems and tools. Just as enterprise software had integration middleware, agents need to plug into ERPs, CRMs, databases, legacy apps, and web services. This layer enables an “AI agent + API” paradigm where agents invoke other software and data sources to accomplish tasks.
   
   
   

4. Security & Governance Layer: Controls for identity, access and policies. This ensures agents operate within set boundaries, handling authentication, role-based access control (RBAC), data privacy, and compliance rules. Corporate IT can set guardrails here (e.g., an agent cannot execute certain actions without approval). As one IBM expert put it, “the challenge becomes transparency and traceability of actions for every single thing the agents do… you need to know exactly what’s happening and be able to track and control it.” In other words, trust and accountability are baked in at this layer.
   
   
   

5. Model Layer: The AI models and model ops that power the agent’s reasoning. This includes large language models (LLMs) or others, plus tools for prompt management, fine-tuning, model selection, and routing. Enterprises often use a mix of models (open-source and proprietary) and need infrastructure for versioning, evaluating, and deploying them safely.
   
   
   

6. Agent OS Layer: The runtimes and frameworks where agent logic lives. This is the “brain” of the agent that handles planning, tool use, step-by-step reasoning, and managing context window. It’s sometimes called the Agent Orchestration layer. Here the agent decides how to break down a goal into actions, when to call an external tool or ask for help, and how to handle errors or new information. Early examples include Microsoft’s open-source Agent Framework and OpenAI’s Agents SDK, which coordinate multiple prompts, tools, and agents in a runtime .
   
   
   

7. Application Layer: The end-user applications or interfaces in which agents operate. These are the agentic apps (or “digital coworkers”) that employees and customers interact with. They could be a chatbot interface, an AI assistant embedded in a CRM, a voice agent on a phone line, etc. This layer delivers the agent’s functionality to users across different vertical domains (finance, healthcare, customer service, etc).
   
   
   

8. Observability & Evaluation Layer: Tools for monitoring agent behavior, performance, cost, and quality. Much like traditional software has APM (application performance monitoring), agents need continuous evaluation and logging. This includes telemetry on actions taken, success/failure rates, detection of errors or “hallucinations,” and feedback loops for improvement. Explainability is crucial here: robust agent stacks log why an agent made each decision, enabling audit trails. Organizations are starting to build internal evaluation suites to test agents on custom criteria and ensure reliability . In essence, this layer treats AI performance and alignment as a first-class operational concern.
   
   
   

Why it matters: This eight-layer model provides a map for CEOs to understand where key capabilities and risks lie. Just as the classic IT stack had hardware, network, application, etc., the agent stack ranges from raw computing infrastructure up to oversight and auditing. It also highlights that winning solutions will likely specialize or excel in one layer but must integrate across all. For example, a vendor might offer superior memory (layer 2) or best-in-class planning algorithms (layer 6), but an enterprise will still need to ensure security (layer 4) and monitoring (layer 8) around that. The agentic ecosystem is likely to consolidate around these layers, meaning platforms that cover all eight in a robust, interoperable way may emerge as leaders. For a CEO, evaluating an AI strategy means asking: Do we have strengths or gaps in any of these layers? Are we using third-party products for some (and if so, do they integrate well with our others)? Ultimately, to move from experiments to scalable deployment, you will need competency in each of these eight domains, from data semantics to model ops to guardrails.

4. Maturity Levels: where the market really stands

Not all “AI agents” are created equal: and most of today’s offerings are far from the autonomous swarms that futurists envision. It’s useful to classify agentic systems by maturity level, to cut through the hype and see what is actually working now versus what’s still experimental. One framework defines five levels:

• Level 1: Prompt-Chaining.
  The simplest form of agent, basically just sequential LLM calls or scripts. These are like advanced chatbots that follow a chain of prompts (e.g. an assistant that takes your input, calls an LLM to transform it, then maybe calls another API). Example: a basic code generation copilot that always follows a fixed sequence (no dynamic planning).
  
  
  

• Level 2: Human-in-the-Loop.
  Agents that can take some autonomous steps but require human approval or validation at key points. Most current “copilots” and AI assistants fall here: they draft content or suggest actions, and a person confirms or edits before execution. Example: AI coding assistants that write code but ask a developer to review changes before committing.
  
  
  

• Level 3: Agentic Workflows.
  True goal-directed agents that can decompose tasks into subtasks and execute multiple steps autonomously, with minimal oversight. These can handle multi-step workflows (often still narrow in scope) and only involve humans for optional review or if something goes wrong. Example: Cognition Labs’ “Devin” agent, an AI software engineer that can take a ticket, plan a solution, write and test code, and propose a fix largely on its own . Devin breaks a coding task into a plan and iteratively implements it, only needing human input for high-level guidance. This level is where cutting-edge prototypes are today.
  
  
  

• Level 4: Fully Autonomous Agent.
  An agent that can be given a goal and will carry it out end-to-end without human intervention, even handling unexpected obstacles. This might include self-correction, learning from mistakes, etc., within its domain. Examples: Rare in production: mostly R&D prototypes (e.g. an experimental AI assistant that can plan an entire marketing campaign, execute it across channels, and optimize it, without a human marketer).
  
  
  

• Level 5: Team of Agents (Autonomous Agent Teams)
  Multiple agents collaborating autonomously, possibly with different specializations, to achieve complex objectives. This is largely theoretical or in research. It envisions agents that can coordinate with each other much like a human team – negotiating roles, sharing information – to solve problems that exceed the capability of any single agent. Example: A research-stage project where a “manager” agent coordinates several “worker” agents (a researcher, a coder, an analyst agent, etc.) to, say, design and run a business project start-to-finish.

  
  

Reality check: As of late 2025, most real-world implementations are stuck between Levels 2 and 3. In practice, the vast majority of “AI agent” products on the market are still closer to advanced assistants that require human judgment at critical junctures, or they handle narrow workflows in a controlled way. Truly autonomous level 4 agents are rarely trusted in production due to reliability issues. In fact, a Carnegie Mellon simulation of a fake company (“TheAgentCompany”) showed that today’s best AI agents succeeded at only ~24% of typical office tasks; even with partial credit for almost-done tasks, they topped out around 34% completion . This underscores that current agents lack the robustness and general reliability to fully replace human workers in most business processes.

The weakest links holding agents back are reliability, reasoning fidelity, and interoperability. They can automate some steps, but often make errors a human wouldn’t (logic mistakes, getting “stuck” on a small obstacle, misinterpreting an instruction) or can’t interface smoothly with all the needed tools out-of-the-box. Automation without accountability is a common critique, an agent might execute quickly, but if it can’t explain its decisions or be held accountable for mistakes, a human overseer is still needed. As IBM’s AI leadership noted, we are seeing “early glimpses” of autonomy, but building agents that handle complex decisions end-to-end will require significant advances in contextual reasoning and rigorous testing for edge cases . In other words, the hype is high: 2025 was often dubbed “the year of the AI agent” – but the reality is that most organizations are in pilot mode, figuring out how to get from level 2 (AI suggestions with human in loop) to level 3 (basic autonomous workflows) in a trustworthy way.

5. Key Tech Trends Shaping 2025–2026

Even with those limitations, progress continues rapidly. Several key trends are emerging that will shape how agentic AI evolves in the next 1–2 years, especially in enterprise settings. These trends highlight where companies are investing and what pain points are being addressed.

5.1  High adoption, low transformation

It turns out there’s a GenAI divide in many businesses: lots of AI pilot projects, but very few yielding real ROI. An MIT report (July 2025) found that despite $30–40 billion poured into enterprise AI, 95% of generative AI pilots delivered no measurable financial impact: only about 5% achieved significant value or ROI (MIT report 2025). In other words, nearly every large company experimented with ChatGPT or built a prototype assistant, but in most cases these remained tech demos or small productivity boosters, rather than true process transformations.

Meanwhile, a Carnegie Mellon study (2025) underscores why many pilots stall: current autonomous agents fail a majority of real-world tasks. The “AgentCompany” benchmark created a fake office and tested leading AI agents on common work chores – scheduling, data entry, research, etc. The best agent succeeded only ~24% of the time, and the average was much lower . Agents often got stuck on trivial issues (e.g. a pop-up on a website prevented them from clicking a button) or misinterpreted instructions. This low task completion rate (<30%) resonates with many anecdotal reports: while employees are enthusiastic about AI assistants, they frequently have to step in and correct or finish the job.

Lesson: The bottleneck to transformation isn’t lack of AI usage: it’s the lack of reliable learning and memory in these systems. Virtually everyone is trying GenAI (over 80% of firms piloted something, per MIT), but simply bolting an LLM onto a workflow doesn’t guarantee success. Without the ability to learn from errors and improve over time, most pilots plateau. Successful projects (the 5%) tend to deeply integrate AI into high-value workflows and incorporate feedback loops to get better. For a CEO, this means scrutinizing AI initiatives not for if they use fancy models, but for how they will actually change a process and whether there’s a path to measurable improvement. As one headline put it, “95% of GenAI projects fail to deliver ROI” being in the 5% requires focusing on sustainable process change, not just flashy demos.

5.2  Memory and context are the new moats

The era of “bigger model = better product” is ending. As foundational AI models from different providers converge in capabilities and become commoditized (you can rent GPT-4, or Claude, or use open-source LLaMA2, all are reasonably powerful), the lasting competitive advantage will come from proprietary data and context. In short, long-term memory, personalization, and contextual understanding are emerging as the durable moats for AI. Bessemer Venture Partners noted in their State of AI 2025 report that “context and memory may be the new moats” for AI products . If your AI agent deeply understands your customers, your history, your proprietary knowledge base in a way no competitor’s AI does then switching away from it becomes very hard (“replacing it feels like starting over”).

We see this trend in practice: Many startups and big tech offerings are racing to build extended context windows, retrieve company-specific data on the fly, and maintain session memory over time. OpenAI, for example, introduced tools to let ChatGPT remember instructions across sessions. Microsoft’s Copilots leverage the user’s documents and emails as context. These efforts recognize that raw model intelligence (the ability to generate fluent text) is necessary but not sufficient, the agent that remembers you (your preferences, the project specifics, the last conversation) will outperform one that starts from scratch each time. Scale is getting commoditized; context is becoming king.

For CEOs, when evaluating AI solutions, the question becomes: does this agent get smarter over time with our data? Is its knowledge base proprietary to us, creating a moat, or is it using the same generic model everyone else can use? The strategic investment may need to shift toward building robust semantic memory layers (see architecture layer 2 above) e.g. vector databases filled with your company’s data, continuously updated. Those who manage to accumulate unique, rich context for their AI will have an edge that pure model improvements can’t easily surmount.

5.3 Evaluation and Data Lineage Become Strategic

In 2023, AI leaders bragged about model accuracy on standard benchmarks (like GPT-4’s exam scores). By 2025, it’s clear that public benchmarks are failing to reflect real-world performance. Enterprises are finding that an AI model’s score on SuperGLUE or MMLU doesn’t guarantee it will reliably handle their customer chats or financial reports. As a result, there’s a shift: organizations are developing internal “eval” suites and data lineage tools to continuously test and trust their AI agents.

Forward-thinking companies now treat robust evaluation as a first-class priority, not an afterthought. IBM’s experts have said that deploying AI without rigorous, interpretable evaluation is like “flying blind” . In enterprise AI, models must be demonstrably safe, interpretable, and accurate for the specific tasks: which means generic metrics aren’t enough . We’re seeing the rise of custom evaluation frameworks and startups (for example, tools like BigSpin, Kiln, etc., that enable continuous feedback loops on model outputs ). Companies are building “AI audit” dashboards to track each agent decision: what data did it use, which version of the model, did it hallucinate, was the outcome approved or corrected? This is akin to unit tests and QA in traditional software, but adapted to AI’s probabilistic nature.

Relatedly, data lineage, tracking the provenance and usage of data through the AI pipeline, is becoming crucial. If an agent produced a faulty output, businesses need to trace why: was it because of a bad data source, or a model error, or an outdated knowledge base? Tools for dataset versioning, prompt tracking, and result logging all feed into this. In regulated industries, it’s even more important: auditors may ask “Show us how this AI made the decision and that it didn’t use unauthorized data.” A recent study emphasized that without traceable data sources, agents “cannot justify their actions or explain why certain results were produced,” undermining accountability . Leading firms are therefore investing in verifiable data sources and lineage ensuring every input to the agent can be checked and every output can be traced back to inputs .

Bottom line: To truly trust and scale AI agents, you must measure them on your terms. CEOs should push for internal benchmarks that matter to their business (e.g. task completion rate, customer satisfaction impact, error rates in specific scenarios) rather than accepting vendor claims at face value. They should also ensure their organization can explain the AI’s decisions after the fact. The winners will be those who build evaluation and monitoring as a core competency, effectively creating an IP in how to test AI (similar to how Toyota’s production system was an IP for manufacturing quality). In 2025–26, expect to see many companies formalizing “Model QA” teams and processes, and treating AI data pipelines with the same rigor as financial data, auditable and controlled.

5.4 Governance and guardrails as differentiators

As agentic AI moves from the lab to the frontline of business, trust becomes a key competitive feature. Enterprises and governments won’t adopt AI agents at scale unless they are confident in safety, ethics, and controllability. Thus, every major platform is now touting built-in governance tools and guardrail SDKs. In effect, trust and compliance have become part of the infrastructure.

For example, AWS launched Bedrock Guardrails, a framework to let developers define policies for generative AI outputs (to filter out sensitive info, enforce factuality checks, avoid toxic content, etc.) . Microsoft introduced “Entra Agent ID” to give every corporate AI agent a unique identity in Azure Active Directory and manage its permissions just like a human employee . They also integrated compliance tools (Microsoft Purview) so that anything an agent does can be logged and subject to company policies . Google and OpenAI similarly have released or announced tools for “guardrails” from OpenAI’s system-level instructions and moderation APIs to Google’s safety filters and policy support in Vertex AI. Even open-source efforts (like Nvidia’s NeMo Guardrails toolkit) are providing ways to programmatically constrain AI behavior.

This means that a big part of the AI agent “platform war” is now about who offers the most robust and enterprise-friendly governance. Cloud providers are essentially competing to assure CEOs and CIOs: “Our agents are safe and controllable, you can trust them with your data and processes.” Indeed, AWS touts that it’s the only cloud with an integrated responsible AI service that works across any model . Microsoft emphasizes its end-to-end compliance and identity management. For a CEO evaluating options, these trust features are not just PR – they should be a deciding factor. Ask: What happens when the AI goes wrong? How do we detect and intervene? Can we set the rules it must follow (e.g. “never execute a payment above $10k without human sign-off”)? The best platforms will have clear, baked-in answers via their guardrail SDKs and dashboards.

In practical terms, governance as infrastructure also implies cross-functional involvement: legal, compliance, and IT teams need seats at the table when deploying agents. It’s not purely an IT project. The organizations that differentiate themselves will be those that turn governance into a strength, leveraging these new tools to deploy AI at scale safely. Trust, after all, can be a market advantage: if your company’s AI is known to be reliable and well-governed, customers and regulators are more likely to support its use. In 2025, building that trust is part of building the tech.

Read this article for an in-depth look at the challenges enterprises face when moving AI from prototype to production.

6. A CEO framework to read the market

How should a CEO make sense of all this and evaluate their own company’s readiness in the agentic AI space? We propose a simple framework with five axes to assess your position and plan next steps. For each axis, ask the key question and consider what leading indicators (or best practices) to watch:

Using this framework, a CEO and executive team can diagnose where they are strong and where they have gaps in their AI agent strategy. For example, you might find your company has decent integration (your AI is plugged into many tools) but low maturity (it’s basically an assisted chatbot), and reliability is unproven (no eval process yet). That suggests investing in advancing capability (perhaps moving to more autonomous pilots in a controlled area) and establishing an AI QA team to start evaluation protocols.

Outcome: By systematically improving along these axes, an organization can craft a roadmap to move from today’s “agent experiments” to tomorrow’s “trusted autonomous systems.” The goal is to reach a state where AI agents are not ad-hoc novelties, but reliable members of the workforce with defined roles, performance metrics, and governance, just like human employees. As one IBM expert noted, 2024 was a year of experimentation, but now “enterprises need to scale that impact… Agents are the ticket to making that happen.” In practice, that means going from a few siloed pilot projects to a cohesive strategy where agents are deployed in multiple departments under a common governance framework, continuously learning and improving. The CEOs that navigate this transition early will position their companies ahead of the curve in efficiency and innovation.

7. The Next frontier: From agentic hype to accountable autonomy

“The next generation of agentic AI won’t just act. It will justify.”

Up to now, much of the AI agent narrative has been about capability: can it do the task? We’re rapidly moving to a phase where accountability and trust are the defining features. Several forward-looking shifts are on the horizon:

• From raw performance to decision reliability. The competitive value of AI agents will shift from how clever their output is to how consistently and correctly they make decisions. In other words, a CEO will care less about whether an agent can write a sonnet or code a webpage (many can), and more about whether it makes zero mistakes in critical processes. Reliability, robustness, and the ability to handle edge cases will define the best agents. AI leaders like Jensen Huang have hinted that the “age of agentic AI” is about agents that can be trusted in the real world, not just impressive demo results . Expect KPIs for AI to include things like error rates, down-time, and compliance incidents, analogous to Six Sigma in manufacturing quality.
  
  
  

• Evaluation and governance as core IP. The organizations that “win” with AI agents will be those that develop proprietary methods and systems for evaluating, tuning, and governing their agents. This will become a sort of internal intellectual property. Think of it this way: the base models might be commodities accessible to all, but your way of controlling them (your secret sauce for making them trustworthy under your business constraints) will set you apart. Some companies are already investing in bespoke evaluation platforms, feedback loops, and safety layers – essentially an AI operations stack that others can’t easily replicate . This mirrors how top tech companies have internal tools and data handling pipelines that give them an edge. For a CEO, this means treating AI oversight not as a burden but as an area for innovation – develop superior methods to keep your AI on track, and that becomes a competitive advantage.
  
  
  

• Leadership decided by memory, context, and policy layers. As discussed, memory and context are becoming moats. Likewise, having a strong policy/guardrail layer builds user and regulator trust. We predict that the tech leaders of 2026 will be those who have nailed these supporting layers of the agent stack. An agent that can retain vast context (within legal and ethical bounds) and follow complex policies will simply be more useful and trustworthy than one that’s a loose cannon. It’s not as flashy as model size, but it’s far more important for enterprise adoption. As BVP noted, when a product “understands a user’s world better than anything else” (context mastery), switching is hard. And when an agent can clearly explain its actions because it’s following a defined policy and citing data sources, people will trust it in high-stakes situations . The future market leaders are investing heavily in long-term memory stores, knowledge graphs, and robust policy engines now.
  
  
  

In summary, while the excitement around agentic AI has been justified by rapid advances, the next chapter is about maturing those capabilities into dependable, auditable systems. The mantra for the coming years will be something like: “It’s great that an AI agent can do X; but can it do X the right way, every time, and show me why?” The technology and companies that can answer “yes” to that question will herald the era of truly accountable autonomy, AI agents we not only marvel at, but genuinely trust and integrate into the core of how we operate.

FAQ

1. Why are AI agents considered the next paradigm after apps?
Because they shift from user-driven apps to goal-driven systems: instead of clicking through software, users delegate objectives to autonomous agents that execute full workflows. Rippletide helps leaders understand how this paradigm reshapes enterprise operations and decision-making.

2. What’s holding enterprises back from scaling AI agents?
Most organizations are stuck between prototype and production due to reliability, governance, and integration challenges. Rippletide identifies these friction points and provides frameworks to move from pilot projects to trusted deployments.

3. How can CEOs use the agent maturity framework?
It helps leaders assess where their company stands from simple copilots (Level 2) to autonomous workflows (Level 3+). Rippletide’s diagnostic tools map maturity levels across capability, memory, governance and evaluation layers.

4. What’s the key to sustainable competitive advantage in agentic AI?
Not just model size but context, memory, and accountability. Enterprises that build strong data semantics and guardrail layers will lead. Rippletide partners with companies to design these reliable agents designed to meet Enterprise standards.

5. How can Rippletide support enterprise readiness for agentic AI?
Rippletide provides market intelligence, readiness assessments and architecture blueprints to help CEOs operationalize AI agents safely and effectively, bridging the gap between experimentation and enterprise-grade adoption.

Sources:

1. Shaw, Frank X. “Microsoft Build 2025: The age of AI agents and building the open agentic web.” Microsoft Official Blog (May 19, 2025)

2. Eusepi, Dion. “The impact of agentic AI on SaaS and partner ecosystems.” CIO.com (Oct 16, 2025)

3. Fahey, James. “The State of AI Agents & Agent Teams (Oct 2025).” Medium (Oct 13, 2025)

4. SuperAnnotate Blog. “Vertical AI agents: Why they’ll replace SaaS and how to stay relevant.” (Jan 31, 2025)

5. Clari Press Release (BusinessWire via AI Journal). “Clari Unveils AI Agents Powered by Revenue Context.” (May 19, 2025)

6. Observe.ai (website). “AI Agents for better customer experiences – Voice-first AI agents… Natural, human-like conversations… Predictable execution.”

7. IBM Think Blog (Ivan Belcic et al.). “AI Agents in 2025: Expectations vs. reality.” (2025)

8. Carnegie Mellon University – SCS News. “Simulated Company Shows Most AI Agents Flunk the Job” (TheAgentCompany study) (June 17, 2025)

9. Article “MIT Report Finds 95% of AI Pilots Fail to Deliver ROI.” (Aug 23, 2025)

10. Bessemer Venture Partners. “The State of AI 2025.” (Sept 2025)

11. Prem Studio (Medium). “LLM Reliability: Why Evaluation Matters & How to Master It.” (Aug 4, 2025)

12. Codatta Blog. “Why the Next Gen of AI Agents Will Rely on Verifiable Data (traceability).” (Oct 13, 2025)

13. IBM Think Blog. Interview – Maryam Ashoori (IBM watsonx Orchestrate). (2025)

14. Microsoft Build Book of News 2025. Azure AI Foundry announcements. (May 2025)

15. AWS Bedrock Documentation. “Amazon Bedrock Guardrails – responsible AI safeguards.” (2025)