What Makes AI Agents Different?
AI tools like ChatGPT, Claude, and Google Gemini have become everyday utilities for marketers, founders, and creators. But in 2025, a new term is reshaping how we think about automation and intelligence: AI agents.
You may already be building multi-step workflows or asking chatbots to generate content — but that doesn’t mean you're using an AI agent. In this article, we’ll break down the evolution from basic AI prompts to fully autonomous, multi-agent systems. Along the way, we’ll explore what makes an AI agent different, why this matters for marketing, and how to prepare your brand for the agentic era.
Level 1: Large Language Models (LLMs) — Prompt In, Output Out
We begin with the tools most of us already use. Large Language Models (LLMs) are what power applications like ChatGPT and Claude. You input a prompt — "Write a product description for a new protein bar" — and the model produces text based on its training.
Key traits of LLMs:
- Passive: They only respond when prompted.
- Stateless: They don’t remember your past prompts unless explicitly designed to.
- Limited access: They can't access private data like calendars, databases, or APIs unless configured to do so.
This is useful, but limited. The user is still doing the planning, iterating, and tool integration.
Level 2: AI Workflows — Logic + Tools + Human Control
When we add APIs and tools into the mix — say, pulling data from Google Sheets, summarising with Perplexity, drafting posts with Claude, and scheduling via Make.com — we’re entering the realm of AI workflows.
These workflows can be incredibly powerful. For example:
- Collect news links daily from a Google Sheet
- Summarise articles using Perplexity
- Generate a LinkedIn post with Claude
- Schedule it for posting at 8 a.m.
But this is not an agent. You, the human, are defining the logic, tools, and sequence. The workflow can't adjust or reason on its own. It’s procedural, not intelligent.
Level 3: AI Agents — Reasoning, Acting, and Iterating Autonomously
An AI agent takes things further. It receives a goal — not just a prompt — and then:
- Reasons about the best way to accomplish it
- Selects and uses tools (e.g. web search, databases, calendars)
- Iterates and critiques its own outputs to improve them
This framework is called ReAct (Reason + Act), and it enables agents to think, decide, act, and evaluate independently.
For example: An AI agent tasked with publishing a marketing post might:
- Find trending news via web search
- Draft a post in the brand’s tone
- Pass the draft to a secondary agent for critique
- Revise based on feedback
- Schedule it for publication
No human intervention is needed beyond the original goal. That’s the leap from automation to autonomy.
Key Agentic Design Patterns
Based on research by Andrew Ng and Crew AI, there are four widely accepted patterns for building intelligent agents:
1. Reflection
Agents review their own outputs and improve them. For instance, one agent writes code, another checks it, and a third suggests improvements.
2. Tool Use
Agents access APIs, perform calculations, fetch data, or even generate images or voice responses. Tool use turns LLMs into functional problem-solvers.
3. Planning
Rather than being told the exact steps, agents plan their own path toward completing a task — selecting tools and sub-tasks as needed.
4. Multi-Agent Systems
Just like human teams, multiple agents can collaborate. One agent may research, another may write, and another may edit or approve. This increases reliability, speed, and scalability.
Case Studies: How Real Brands Are Using AI Agents
InkyBot: A No-Code Calendar Assistant
Using n8n and OpenAI, a developer built an agent that:
- Reads your Google Calendar
- Accepts voice or text via Telegram
- Prioritises your day and creates new calendar events
No code. Fully agentic. Easily extensible.
Unilever: Ethics + Autonomy
Unilever released a Responsible AI Framework, ensuring that its agents use customer data responsibly, self-critique marketing copy, and follow compliance rules. This helped the brand earn positive citations in Gemini AI summaries.
Crew AI: Multi-Agent Systems in Research
In complex environments, Crew AI orchestrates teams of agents: researcher, summariser, planner, and strategist — each with access to specific tools. Results outperform single-model systems in speed and relevance.
SaaS to AaaS: The Business Model Shift
Y Combinator predicts: For every SaaS company today, there will be a corresponding AI agent business tomorrow.
This shift from Software-as-a-Service (SaaS) to Agent-as-a-Service (AaaS) is already underway:
- Canva → Agent-based design assistant
- Salesforce → AI CRM agents that write and optimise campaigns
- Calendly → Autonomous meeting planner with task awareness
Marketers must now ask: how will my tools evolve — or be replaced — by autonomous agent infrastructure?
GEO + Ethics: Discoverability in the Age of AI Agents
Generative Engine Optimisation (GEO) is how brands get found in AI-driven summaries and responses. Ethical and transparent agents have a much higher chance of appearing in:
- ChatGPT answers
- Google Gemini Overviews
- Perplexity summaries
To be GEO-ready:
- Include structured data and schema markup
- Maintain a public AI ethics policy
- Clearly label AI-generated content
- Optimise for intent-driven, question-based queries
This is no longer just technical SEO — it's ethical architecture for discovery.
Final Thoughts: From Tools to Autonomous Teammates
AI agents are not a futuristic concept. They’re already here — researching, writing, building, iterating, and improving. The key to staying competitive in 2025 isn’t simply using AI — it’s building systems where AI can act, reason, and improve alongside your team.
At Anjin Digital, we specialise in designing agentic workflows, multi-agent systems, and GEO-aligned content strategies — helping your business stay visible, scalable, and future-ready.
Sources:
- Kome AI – https://kome.ai
- Crew AI x DeepLearning.AI – https://learn.deeplearning.ai/crew-ai
- n8n – https://n8n.io/blog/ai-agent-workflows
- Y Combinator – https://www.ycombinator.com/blog/ai-agents
- Unilever – https://www.unilever.com/ai-responsibility-framework-2025/
