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/How Agentic AI Platforms Are Taking Shape. An overview of agent categories and their functionalities
In less than a decade, the field of artificial intelligence has evolved from statistical pattern matching to reasoning systems that can plan, execute, and adapt.
The first generation of AI tools — think ChatGPT in late 2022 or Google Bard in early 2023 — were revolutionary because they could converse. They understood natural language and generated coherent responses faster than any search engine or chatbot that had come before.
But something changed between 2024 and 2025.
Language models stopped being purely reactive. They began to act.
This shift was catalysed by the introduction of agentic frameworks — architectures that allow large language models (LLMs) not just to produce text, but to perform actions across connected systems, files, and APIs.
The result is the Agentic Era of AI, where software agents can interpret intent, orchestrate workflows, and interact with real-world data.
They are no longer confined to a chat window — they can browse, write code, trigger automations, and coordinate multi-step tasks.
This transformation has led to the emergence of what we call the Agent Ecosystem — a five-layered stack of technologies that collectively enable intelligent action across both consumer and enterprise domains.
Each layer of this stack plays a distinct role — from the visible chat interfaces we interact with daily, down to the invisible automation frameworks that keep everything synchronised.
Together, they form a new kind of computing architecture: one driven by intention rather than instruction.
To understand how modern AI systems collaborate, we need to zoom out and view them as a layered network — not as isolated products.
Each layer represents a stage of agency and autonomy, ranging from simple reasoning to multi-step execution and orchestration.
Agentic LLMs with Frontend Tools: These are the visible conversational control planes — ChatGPT, Claude, Copilot — that interact directly with the user.
Chatbot / Knowledge-Based Agents: Focused on information retrieval and summarisation, they serve as static domain experts.
Browser-Action / Web-Interaction Agents: Agents that can see, navigate, and interact with live websites.
Developer / SDK / Build-Your-Own Agents: Toolkits that allow engineers to build custom agentic systems and integrate models into software.
Workflow / Automation Agents: The operational infrastructure that triggers, monitors, and connects the flow of data between tools.
This top layer of the agentic ecosystem is where artificial intelligence finally becomes tangible — where conversation turns into action. It is the frontline of the agentic revolution, the interface that most people experience directly, whether at home, in the workplace, or inside an enterprise system.
Traditionally, users interacted with software by navigating menus, clicking buttons, or writing code. The defining transformation of this layer is that natural language itself becomes the operating system. A user no longer needs to understand how to use a complex tool; they simply need to describe what they want. The AI agent interprets the intent, identifies relevant data, selects the right tools, and executes the required commands — seamlessly and autonomously.
This layer brings together conversational intelligence and actionable execution, creating what can best be described as a “control room” for modern AI systems. The human sets the direction; the agent handles the details.
ChatGPT (OpenAI) has evolved from a conversational model into a complete operating environment for intelligent execution. Through its Agents and Actions, it can now call APIs, connect through MCP (Model Context Protocol), and perform cross-platform operations — managing data across applications such as Slack, Notion, Jira, or even internal CRMs. For example, a project manager could simply type:
“Summarise all tasks marked as ‘In Progress’ in Jira and create a weekly status update in Notion.”
The agent securely retrieves the data, processes it, and delivers a formatted report — all without the user needing to open a single dashboard.
Claude (Anthropic) focuses on deep file understanding and desktop integration. The Claude Desktop app allows users to drag an entire folder of reports, briefs, or codebases into the chat window and instruct the model to summarise, rewrite, or refactor the contents. Its local processing capabilities make it particularly useful for compliance-sensitive environments that require on-device data handling.
Microsoft Copilot represents the enterprise implementation of this paradigm. Integrated across Office, Teams, SharePoint, and GitHub, Copilot turns everyday productivity tools into intelligent collaborators. A user could say,
“Draft a financial summary using this quarter’s Excel sheet and generate a PowerPoint for tomorrow’s meeting,”
and the system automatically performs data analysis, creates visuals, and structures the presentation.
Recently, Microsoft announced that Copilot now supports both GPT-5 and Anthropic’s Claude models, giving enterprises flexibility to use the best reasoning engine for each task.
Google AgentSpace brings the same power to the Google ecosystem. It allows users to query Drive, Docs, Sheets, and Gmail within a unified conversational workspace. For example, a marketing manager might ask,
“Find all campaign briefs from Q2, extract key metrics, and create a summary slide deck,”
and AgentSpace orchestrates the retrieval, analysis, and presentation using its agentic connectors.
These systems are rapidly transforming both enterprise operations and personal productivity. Within the enterprise, agentic LLMs can now connect to CRM, ERP, HR, and Financial Systems, retrieve insights, update records, or trigger workflows — tasks once limited to specialist users. A sales manager could ask,
“Show me all opportunities closing this week and draft a follow-up email for each client,”
and the agent would generate customised messages and log them in the CRM.
In IT or operations, an agent could connect to Jira or ServiceNow to create tickets, check server status, or summarise incidents. In finance, it could extract ledger entries, reconcile them with forecasts, and prepare a variance report.
For individual users, the same logic applies on a smaller scale: writing emails, scheduling meetings, generating spreadsheets, designing Canva presentations, or summarising Teams and Slack conversations — all triggered through a simple chat.
The power behind these capabilities lies in MCP — a universal connector that allows agents to securely interact with virtually any system, API, or data source. With MCP, an agent can read and modify data, invoke specialised sub-agents, and coordinate workflows across different environments.
These agentic LLMs also bring together memory, reasoning, and dynamic orchestration. They remember previous context, learn from results, and adjust their strategies accordingly. They can break down complex objectives into smaller executable tasks, discover available agents dynamically, and execute them autonomously — often pausing to request user confirmation before making critical changes.
In essence, this layer is where the promise of AI becomes reality: a seamless interface where human intention is translated into digital execution. Whether drafting documents, managing enterprise workflows, or automating creative work, agentic LLMs now serve as the bridge between thought and action — marking the beginning of a new era of productivity, collaboration, and intelligence.
Visual showing ChatGPT, Claude, Copilot, and AgentSpace connected to enterprise systems (CRM, ERP, Enterprise Databases) through chat lines labeled “Intent → Action → Result.
(Precursors to True Agency — Turning Documents Into Dialogue)
Before AI systems could plan and act, they needed to read, reason, and respond. The second layer of the Agentic Stack — Chatbot / Knowledge-Based Agents — represents the earliest and most foundational phase of intelligence: understanding.
These were the precursors to modern agentic systems. While they lack autonomy, they pioneered the fundamental capability that every agent still relies on today — the ability to query information using natural language. In essence, they transformed static data into interactive, explainable knowledge.
Before the advent of orchestration frameworks like Model Context Protocol (MCP) or SDK-based tool calling, most AI systems were standalone assistants trained on a fixed corpus. Their purpose was simple but revolutionary: to let users ask questions about specific documents, databases, or topics — and receive coherent, human-like answers.
Think of these systems as the first layer of human-AI communication, capable of reading large knowledge sets and explaining them conversationally. They made information searchable, interpretable, and personalised, long before agents learned to “do.”
The user’s experience was no longer about searching through pages of results — it was about asking and understanding. A compliance officer could ask,
“What does our internal policy say about remote work eligibility?”
and the chatbot would pull the exact clause from a policy document and summarise it in plain English.
This shift — from browsing to conversing — was the first great leap in AI-assisted productivity.
Three major platforms define this era of knowledge-driven AI.
Custom GPTs (OpenAI)
These are domain-specific chatbots built on top of OpenAI’s GPT models. By providing a curated dataset — company documents, handbooks, manuals, or reports — users could create a custom version of ChatGPT fine-tuned to their needs. For example, a law firm might deploy a “Contract Analyst GPT” that explains specific clauses, or a university might use a “Policy GPT” trained on academic procedures.
Microsoft Copilot Agents
While Microsoft Copilot now spans enterprise workflows, its initial versions acted primarily as contextual Q&A assistants. A Copilot Agent inside SharePoint or Teams could retrieve internal knowledge, summarise meeting notes, or explain governance policies. These were some of the earliest “trusted” AI implementations in corporate environments — running within Microsoft’s secure, auditable ecosystem.
Google NotebookLM
NotebookLM pioneered the concept of an interactive notebook. Users upload PDFs, slides, or articles, and the model turns them into a dynamic, queryable workspace. Researchers could upload entire study archives and ask, “Summarise the methodology of all 2024 papers about solar energy,” receiving concise, cross-document answers.
Collectively, these systems became the intellectual groundwork for agentic reasoning — the part that thinks before doing.
The strength of these early agents lies in their ability to convert static knowledge into dynamic insight. They excel at interpreting, summarising, and explaining, but they stop short of taking real-world action.
In practice, they are:
Excellent at contextual reasoning: They understand questions and provide nuanced answers.
Auditable and predictable: They don’t alter data or trigger actions, making them ideal for compliance environments.
Independent by design: Each one operates in isolation — typically trained on a single dataset or repository.
But herein lies their limitation: they cannot talk to each other.
At present, most knowledge-based agents are siloed. A Custom GPT trained on HR data cannot automatically collaborate with another GPT trained on IT policy. Similarly, a NotebookLM instance can’t be automatically discovered or invoked by a ChatGPT Agent. There is no shared protocol for these chatbots to interoperate, share context, or exchange answers.
This means that while they provide remarkable understanding within their defined domain, they lack the extendibility and connectivity of modern agentic systems. They represent islands of intelligence — valuable, but self-contained.
Despite these limitations, this layer remains foundational. Every higher layer — from browser agents to automation workflows — still relies on the comprehension, summarisation, and contextual reasoning developed here.
Knowledge-based agents are the interpreters of the AI world. They translate complexity into clarity. In enterprises, they are used daily to:
Summarise HR or compliance documents.
Explain financial policies.
Surface research findings for analysts.
Guide employees through complex onboarding or IT workflows.
And because they are constrained, they are trusted.
Their independence makes them safe to deploy in regulated sectors — healthcare, education, law, or finance — where guardrails and explainability are essential.
The next frontier is interoperability. As the agentic ecosystem matures, the ability for LLMs to discover and communicate with other knowledge agents through frameworks like MCP will redefine this layer. Instead of isolated bots, we’ll have a knowledge mesh — a network of reasoning agents capable of collaborating dynamically.
In this future, ChatGPT might call upon a NotebookLM agent to retrieve financial policy insights, combine it with a Copilot Agent’s meeting summary, and synthesise a compliance-ready report — all automatically.
Until that point, the knowledge-based layer stands as the historical and intellectual bridge — the foundation upon which modern agentic systems are built. These are the early architects of understanding, the primitive forms of AI that first gave humans the power to talk to their data — and have it talk back.
“The evolution from passive knowledge to active intelligence.”
(The Bridge Between Knowledge and Real-World Action)
If Layer 2 represented the mind of artificial intelligence — systems that could read, interpret, and summarise information — then Layer 3 marks the point where that understanding becomes active. It is here that AI systems step beyond static knowledge into the living, ever-changing environment of the internet. This layer — the Browser-Action and Web-Interaction Agents — forms the connective tissue between digital reasoning and digital execution.
For the first time, AI agents can see, navigate, and interact with the live web in real time. They are no longer limited to curated datasets or internal documents; they can open pages, click links, fill out forms, and extract or transform live data — much like a human researcher or analyst would.
Until recently, large language models (LLMs) were confined to a sealed world: whatever they knew was frozen at the time of training. If you asked about a new company announcement, an updated regulation, or a changing stock price, the model could only speculate.
The introduction of browser-interactive agents fundamentally changes that. These systems give models the ability to observe and act within the live web, pulling in up-to-date information directly from online sources.
Instead of being “omniscient but outdated,” these agents have situational awareness. They can navigate, verify, and synthesise data in real time — combining reasoning with observation.
Imagine a journalist asking,
“Summarise all government press releases about digital safety in the past week and generate a list of new policy recommendations.”
A browser-action agent can now perform that research autonomously: it visits official websites, reads the content, summarises key points, and produces a clean report — all within minutes.
This is the moment AI became a true digital participant, not just an observer.
Several key technologies have emerged as leaders in this space, each advancing the concept of AI agents capable of navigating the web.
ChatGPT Agent
OpenAI’s ChatGPT Agent introduced the concept of a virtualised browsing environment. Within the chat interface, the model can visit URLs, read pages, extract data, and generate detailed reports. This enables real-time research — for instance, checking updated pricing information, compiling competitor analyses, or reading recent news articles. For knowledge workers, this means no more switching between tabs; the browser comes to the AI.
Claude for Chrome
Anthropic took a different approach by embedding its model directly into the user’s browser environment. With Claude for Chrome, the AI can access open tabs, see what the user is viewing, autofill forms, or summarise entire pages. This blurs the line between assistant and collaborator — a tool that not only reads the web but works alongside you as you browse it. It’s especially powerful for research-heavy or administrative tasks: filling online forms, reviewing contracts, or summarising documentation.
GenSpark AI Browser
GenSpark represents the next conceptual leap — a browser built natively around agentic principles. Unlike plug-ins or extensions, GenSpark treats the AI as a first-class operator. It can run local models, open multiple tabs, perform comparisons, and execute actions in autopilot mode. Imagine a browser that independently checks shipping rates across sites, verifies supplier credentials, or compares university program details. This is a preview of where the web itself is headed: AI-native browsing.
Perplexity Comet
Perplexity Comet redefines how users consume web content rather than how agents act upon it. As users browse, Comet continuously answers questions, provides summaries, and displays contextual citations — making it the ultimate exploration companion. Instead of users performing manual searches, the agent provides ongoing, contextually relevant commentary. It’s the AI equivalent of a research assistant quietly explaining what you’re reading, page by page.
In this layer, the browser evolves from a viewing portal into a collaborative workspace.
Historically, web browsers were passive — users navigated, clicked, and consumed. But with AI agents integrated into or running alongside the browser, it becomes an interactive environment for reasoning and execution. The browser can now:
Conduct multi-tab research autonomously.
Summarise and cross-reference live information.
Fill out web forms or update online records.
Verify claims and cite trustworthy sources.
Deliver live reports that blend web data with internal knowledge.
For research, journalism, and data collection, this represents a revolutionary leap. Analysts no longer have to gather data manually; the agent does it continuously, updating as new information emerges.
The browser-action layer has immediate and powerful implications for both enterprise and individual users.
Market Research: Analysts can ask agents to scan competitor websites, compare product features, and compile summaries automatically.
Compliance Monitoring: Legal teams can track updates to government or industry regulations without manually checking multiple portals.
E-commerce and Finance: Agents can pull live pricing or currency exchange rates, compare vendor options, and generate reports.
Recruitment: HR professionals can use agents to find candidate profiles, summarise résumés, or compile shortlists from multiple platforms.
Academia and Journalism: Students or reporters can automate citation generation, cross-verify facts, and collect references from credible sources.
On the personal side, browser agents can help users with day-to-day actions — renewing subscriptions, managing travel bookings, or extracting receipts from email-based invoices.
While this layer feels almost magical, it remains transitional. These agents still require structured prompts and human oversight. Their actions, though intelligent, are guided rather than autonomous.
At present, browser agents operate in isolation — each tied to a specific model or vendor. ChatGPT’s browsing agent, for instance, cannot automatically call Claude for Chrome to verify a citation or perform a secondary search. This is where future layers like MCP (Model Context Protocol) will become essential, enabling cross-agent collaboration and automated discovery of browser tools.
Once interoperability arrives, the browser will no longer just host a single assistant — it will become the operating surface for the agentic web, where multiple AI systems interact dynamically with live data, each specialising in its domain but sharing one mission: to act intelligently and coherently on behalf of the user.
An AI avatar reviewing multiple browser tabs, filling a form, and creating a summary window; labeled arrows show “Observe → Extract → Summarise → Deliver.”
(The Engineering Core of the Agentic Ecosystem)
Beneath every polished AI interface — the ChatGPTs, Copilots, and Claudes of the world — lies a far more technical layer. It is here that developers, architects, and researchers define how intelligence behaves. This fourth layer of the agentic stack, the Developer / SDK / Build-Your-Own Agents layer, is where the real engineering happens. It’s the layer that transforms large language models from raw text predictors into structured, goal-oriented systems capable of reasoning, planning, and acting.
This layer represents the infrastructure of agency. While upper layers interact with users, this one defines how those interactions are made possible — how an agent calls a tool, remembers context, interprets feedback, or stays within its guardrails. Every function, every action, and every decision in an AI system is rooted in the SDK or framework that governs it.
The SDK layer is essentially the operating framework for intelligence. It provides developers with tools, interfaces, and abstractions to control an agent’s cognition and behaviour. Without it, LLMs would remain static — capable of generating language but unable to make structured decisions or connect to the systems that matter in the real world.
At its core, this layer answers one question: How do we make models act intelligently, safely, and consistently across different environments?
Frameworks in this space allow developers to:
Chain reasoning steps together logically.
Manage contextual memory between user interactions.
Define which external APIs or databases the agent can safely access.
Establish workflows, triggers, and hand-offs between multiple agents.
These SDKs are not about conversation — they are about control and orchestration.
A handful of frameworks dominate this emerging field, each taking a slightly different approach to how developers can build, govern, and extend AI agents.
AI SDK by Vercel
Among the newest entrants, Vercel’s AI SDK stands out for its open, provider-agnostic architecture. Built for modern JavaScript and TypeScript environments, it allows developers to combine multiple models — OpenAI, Anthropic, Gemini, Mistral, and Cohere — within a single workflow. This is a major step forward in multi-model orchestration.
Developers can now use one model for reasoning, another for creative writing, and another for quantitative analysis — all in the same session. It bridges previously siloed ecosystems, allowing composable intelligence across model providers. Because it’s native to web development frameworks like Next.js, it’s quickly becoming the SDK of choice for front-end and serverless AI integration.
Claude Agent SDK (Anthropic)
The Claude Agent SDK is designed for depth of integration. It allows organisations to create Claude-based agents that connect deeply with their own environments — file systems, internal APIs, or secure local data. This makes it especially attractive for enterprises needing compliance-grade AI. Developers can embed Claude as a reasoning core inside complex applications, giving it controlled access to documents, systems, or tools, while maintaining privacy and safety.
ChatGPT Agent Kit (OpenAI)
The ChatGPT Agent Kit extends OpenAI’s ecosystem by giving developers the power to connect external APIs directly to ChatGPT Agents. These connections are equipped with strict permissioning and safety layers — known as “guardrails” — ensuring that agents only perform intended actions. It introduces “Actions,” “Triggers,” and “Custom Tools,” allowing the creation of structured workflows within ChatGPT’s agentic environment.
OpenAI Agents SDK (formerly Swarm)
Where the ChatGPT Agent Kit focuses on individual agents, the OpenAI Agents SDK goes a step further by enabling cooperative intelligence. Formerly known as Swarm, this SDK provides an infrastructure for multi-agent collaboration, allowing multiple specialised agents to communicate, share context, and divide responsibilities. A research assistant agent might gather data, a writing agent might draft a report, and a validator agent might review it — all within the same environment.
LangChain
LangChain remains the backbone of the open-source agentic movement. It introduced many of the core abstractions — such as chains, tools, and memory — that are now standard in the industry. LangChain excels in creating structured reasoning loops and integrating external data through retrievers and vector stores. It’s widely used for experimental and academic projects but is increasingly being adopted in production pipelines as a lightweight orchestration layer.
The SDK layer is what transforms LLMs from chatbots into programmable entities. It defines how models think, remember, and act — and, crucially, how developers can extend or constrain that intelligence.
This layer governs:
Tool calling: how agents access APIs or utilities.
Memory management: how they recall past interactions or retrieved data.
Reasoning structure: how they decompose complex problems into steps.
Safety and governance: how execution is controlled within organisational boundaries.
In enterprise environments, this means AI can safely access CRMs, ERPs, or document repositories through SDK-defined connectors, all while following access control and audit protocols.
Every chat interface — from Microsoft Copilot to ChatGPT Enterprise — ultimately depends on these SDKs. They are the blueprint layer: invisible to the user, indispensable to the system. Without them, there would be no coordination between reasoning, memory, or action; no safe execution of workflows; and no way to scale intelligence beyond conversation.
Layer 4, in essence, is the engineering foundation of the agentic era — the toolkit that gives AI both its autonomy and its discipline.
and show a layer above with how these SDKs have tool call, memory and reasoning and how they connect are called by agentic LLMs eg Claude, Open AI,
(The Invisible Infrastructure That Powers the Agentic World)
At the base of the agentic ecosystem lies the foundation of intelligent automation — a layer that doesn’t converse, reason, or plan like its higher counterparts, but without which the rest of the ecosystem simply wouldn’t function. These are the Workflow and Automation Agents, the silent operators that handle the background tasks, the triggers, and the data movement that make large-scale AI orchestration possible.
This layer isn’t about intelligence in the cognitive sense — it’s about execution, reliability, and scale. It provides the machinery that turns AI-driven intention into repeatable, measurable, and auditable action. While upper layers decide what should be done, this one ensures it gets done — correctly, consistently, and continuously.
Automation platforms like Zapier AI Agents, n8n Agents, and now OpenAI’s Agent Builder form the connective tissue that unites disparate systems into one coherent network.
These platforms monitor digital events — the everyday triggers of modern enterprise life:
A new email arrives in Gmail.
A support ticket is created in Zendesk.
A lead is added to Salesforce.
A document is uploaded to Google Drive or SharePoint.
Each of these events can trigger a chain of automated actions. Traditionally, these were linear “if-this-then-that” sequences. But with the integration of large language models, these workflows are now capable of reasoning and adapting dynamically.
For example, Zapier can now use GPT to read a support ticket, categorise it by topic, summarise the request, send a personalised follow-up in Slack, and record the interaction in Notion — all autonomously. What used to require multiple human handoffs now happens in real time, across multiple systems, guided by AI logic.
n8n, an open-source alternative, brings this same power into a self-hosted and customisable environment, allowing enterprises to build their own private automation networks — critical for organisations handling sensitive or regulated data. Developers can design branching workflows, integrate internal APIs, or even host reasoning agents within the flow.
In both cases, the AI acts as a logic layer — interpreting, classifying, and generating decisions between mechanical tasks.
A major evolution within this layer is the introduction of OpenAI’s Agent Builder — an all-in-one framework designed to make it easier for developers and non-developers alike to create, deploy, and orchestrate custom AI agents.
The OpenAI Agent Builder goes beyond traditional automation by allowing developers to:
Integrate reasoning and tool-use directly into workflows.
Define structured multi-step processes where models can call APIs, access data, and make decisions.
Manage multiple agent configurations under a single governance layer.
Deploy agents with specific capabilities — e.g., research, reporting, compliance — and integrate them into enterprise environments.
Where Zapier and n8n act as external automation platforms, the Agent Builder embeds workflow intelligence directly within OpenAI’s ecosystem. It provides a modular interface for building agents that can perform autonomous reasoning, data manipulation, and action sequencing — all powered by the same APIs used in ChatGPT and GPT-5.
This convergence between workflow automation and reasoning marks a turning point. It means AI-driven workflows can now plan, reason, and act across multiple systems — all governed by the same foundational logic.
The real power of this layer lies in its ability to scale human intent across systems.
In enterprise environments, Workflow Agents are being used to:
Automate customer service: summarising tickets, tagging urgency, escalating high-priority issues.
Streamline HR processes: onboarding new employees, sending automated policy briefings, and managing access control.
Support finance operations: generating expense reports, reconciling transactions, and updating dashboards.
Maintain CRM hygiene: enriching records, syncing data between platforms, and logging communications automatically.
Enhance marketing operations: monitoring campaign metrics, generating summaries, and triggering creative updates.
Because these workflows can be designed to incorporate LLM reasoning steps, they no longer require explicit rule-based logic. A workflow can “decide” whether a message is urgent, whether a lead fits a specific profile, or how to summarise a dataset — tasks that were once purely human.
The Workflow / Automation layer is the backbone of the agentic stack. It ensures that all upper layers — from ChatGPT and Claude to SDK-based agents — can operate at enterprise scale.
Without this layer, even the most advanced AI systems would remain trapped in isolation, capable of reasoning but unable to act in a sustained, repeatable, or integrated way.
By combining traditional automation reliability with the adaptive reasoning of modern LLMs, platforms like Zapier, n8n, and OpenAI’s Agent Builder have transformed automation into something far more powerful: a living network of intelligent processes, constantly listening, reasoning, and acting — invisibly, but indispensably.
OpenAI Agent Builder Example
The five layers of the agentic ecosystem are not competitors — they are collaborators.
Each plays a complementary role in enabling autonomous intelligence.
Imagine this workflow:
A user chats with ChatGPT (Layer 1) and requests a “weekly performance summary.”
ChatGPT delegates the task to a Claude Agent SDK-based backend (Layer 4).
The SDK retrieves data using APIs, and if needed, accesses the web through browser agents (Layer 3).
The collected information is refined using a Custom GPT trained on internal guidelines (Layer 2).
Finally, the data is logged and distributed using Zapier or n8n (Layer 5).
This is not speculative — this architecture already exists.
Dozens of enterprise systems are being redesigned around these principles right now.
image that has the full agentic stack in motion with End-to-end flow diagram connecting Chat Interface → SDK Layer → Browser Agents → Automation → Output.
The agentic ecosystem is more than a trend; it’s the new foundation of digital work.
In the 1990s, every business rushed to build a website.
In the 2010s, every business built a mobile app.
In the 2020s, every business will deploy an agent — a personalised, autonomous interface between users, data, and systems.
The implications are far-reaching:
Productivity: Employees can focus on strategysta and creativity while agents handle the repetitive work.
Governance: Agent SDKs introduce traceability and permission models that make automation safer.
Interoperability: The rise of standards like MCP ensures agents can talk to each other across platforms.
Education: Universities and training programs will need to teach prompt design, orchestration logic, and multi-agent architecture.
Understanding the layered ecosystem helps decision-makers see where their organisation fits — whether they’re building agents, deploying them, or governing their use.
The natural evolution of this ecosystem will be the rise of meta-agents — supervisory systems that coordinate multiple agents autonomously.
A meta-agent could assign tasks to sub-agents, validate outputs, and adjust plans dynamically — effectively functioning as an intelligent operations manager.
Imagine ChatGPT delegating a research assignment to Claude, passing results to an SDK agent for data cleaning, invoking n8n to automate publishing, and reporting back to the user — all without explicit human direction.
We are already seeing the early versions of this through OpenAI’s “Swarm” experiments and Claude’s sub-agent orchestration features.
Over time, these meta-agents will blur the boundaries between software applications. Instead of switching between apps, you’ll simply describe your objective, and your meta-agent will select, configure, and coordinate the right tools.
By mid-2025, every major AI vendor occupies a defined role in this map:
OpenAI leads in conversational orchestration with ChatGPT Agents and Agent Kit.
Anthropic provides developer depth with the Claude Agent SDK and desktop integrations.
Microsoft integrates agentic intelligence across productivity and developer ecosystems.
Google leverages its suite of workspace tools through AgentSpace.
Vercel acts as the connective tissue, enabling developers to build multi-model agents.
Together, they’re not competing to replace each other — they’re defining an interlocking architecture that represents the next computing paradigm.
The agentic future is not speculative. It’s already here — unfolding quietly across every chat interface, SDK, and workflow automation tool we use daily.
Agents are no longer something we talk to.
They are becoming the systems we work through.