Manthan Patel

LLMs are AI models, but not all AI models are LLMs.

Building upon traditional approaches, these eight specialized models advances AI's ability to understand, reason, and generate across different domains and modalities.

Here's architectures of these 8 state-of-the-art models:

1️⃣ LLMs (Large Language Models)
These foundational models process text token-by-token, enabling everything from creative writing to complex reasoning.

2️⃣ LCMs (Large Concept Models)
Meta's newer approach encodes entire sentences as “concepts“ in SONAR embedding space, transcending word-level processing.

3️⃣ VLMs (Vision-Language Models)
These multimodal combine visual and textual understanding to interpret images and generate text about them.

4️⃣ SLMs (Small Language Models)
Compact yet powerful models optimized for edge devices with tight energy and latency constraints.

5️⃣ MoE (Mixture of Experts)
These models activate only relevant expert networks per query, dramatically improving efficiency while maintaining performance.

6️⃣ MLMs (Masked Language Models)
The OG bidirectional models that look at both left and right context to understand meaning in text.

7️⃣ LAMs (Large Action Models)
Emerging models that bridge understanding with action, executing tasks through system-level operations.

8️⃣ SAMs (Segment Anything Models)
Foundation models for universal visual segmentation with pixel-level precision.

Here's how these specialized architectures differ from traditional approaches:

Traditional AI:
- One model architecture applied to many tasks
- Often excels in one area but underperforms in others
- Requires significant compute and data for general capabilities

Specialized Architectures:
- Purpose-built for specific modalities and tasks
- Optimized for particular constraints (speed, size, precision)
- Open up new capabilities like concept-level understanding, visual segmentation, and action execution

Understanding these distinctions is essential for selecting the appropriate model architecture for specific applications, making more effective and contextually appropriate AI interactions.

These specialized models aren't alternative approaches; they're redefining technologies.

✅ Process information in ways that match specific tasks and domains
✅ Optimize for different constraints like size, speed, accuracy, and multimodality
✅ Generate more reliable, contextual, and useful outputs for targeted applications

Matching the right architecture to the right task is essential. It saves time, boosts productivity, and creates a more natural flow in AI-human interactions.

Over to you: What specialized AI architecture do you think would benefit your work the most?

Everyone's building AI agents, but few understand the Agentic frameworks that power them.

These two distinct frameworks are the most used frameworks in 2025, and they aren't competitors but complementary approaches to agent development:

𝗻𝟴𝗻 (𝗩𝗶𝘀𝘂𝗮𝗹 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻)
- Creates visual connections between AI agents and business tools
- Flow: Trigger → AI Agent → Tools/APIs → Action
- Solves integration complexity and enables rapid deployment
- Think of it as the visual orchestrator connecting AI to your entire tech stack

𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 (𝗚𝗿𝗮𝗽𝗵-𝗯𝗮𝘀𝗲𝗱 𝗔𝗴𝗲𝗻𝘁 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻) by LangChain
- Enables stateful, cyclical agent workflows with precise control
- Flow: State → Agents → Conditional Logic → State (cycles)
- Solves complex reasoning and multi-step agent coordination
- Think of it as the brain that manages sophisticated agent decision-making

Beyond technicality, each framework has its core strengths.

𝗪𝗵𝗲𝗻 𝘁𝗼 𝘂𝘀𝗲 𝗻𝟴𝗻:
- Integrating AI agents with existing business tools
- Building customer support automation
- Creating no-code AI workflows for teams
- Needing quick deployment with 700+ integrations

𝗪𝗵𝗲𝗻 𝘁𝗼 𝘂𝘀𝗲 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵:
- Building complex multi-agent reasoning systems
- Creating enterprise-grade AI applications
- Developing agents with cyclical workflows
- Needing fine-grained state management

Both frameworks are gaining significant traction:

𝗻𝟴𝗻 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺:
- Visual workflow builder for non-developers
- Self-hostable open-source option
- Strong business automation community

𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺:
- Full LangChain ecosystem integration
- LangSmith observability and debugging
- Advanced state persistence capabilities

Top AI solutions integrate both n8n and LangGraph to maximize their potential.
- Use n8n for visual orchestration and business tool integration
- Use LangGraph for complex agent logic and state management
- Think in layers: business automation AND sophisticated reasoning

Over to you: What AI agent use case would you build - one that needs visual simplicity (n8n) or complex orchestration (LangGraph)?

I've compiled 10,000+ Make.com templates that I use for automating my clients' businesses.

I'm giving away all these templates that get clients paying me $5K/month, for FREE.

It has hundreds of automation templates, segmented by 15+ categories:

👉 AI Tools
👉 Sales & CRM
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👉 Surveys and Document
👉 IT Systems
👉 Business Operations
👉 Website Building

And honestly, I wouldn't even call this a template pack.

It's literally every automation I've built over 2 years of running a 6-figure agency.

⚠️ 10,000+ Make Automation- https://tally.so/r/31GQXp

Already 150+ agency owners who scaled to 6-figures are using these exact templates.

2025 is the Year of AI Agents, not just standalone LLMs.
 
Anthropic has been using this new approach called Multi-Component AI Agents with Feedback Loops.
 
AI Agents go beyond basic LLMs with structured parts that work together, letting them solve problems on their own and get better with practice.
 
Here's how AI Agents work:
1️⃣ Perception Layer
Agents take in information through special modules that understand context and track what's happening, helping them see the full picture.
 
2️⃣ Cognitive Core
The thinking and planning parts work together, mixing logical reasoning with goal-setting to make smart choices.
 
3️⃣ Execution Framework
A dedicated action layer picks the best moves and uses outside tools, while checking how well things are working.
 
4️⃣ Learning Loop System
Key feedback paths connect what happened to memory storage, creating a cycle that makes the agent better over time.
 
5️⃣ Multi-Tool Integration
Special outside tools like Web, Code, and API access let an agent do more than what's built in.
 
Whether you're handling complex workflows or tackling multi-step problems, AI Agents deliver better results through their connected design, giving you more reliable performance and flexible responses.
 
Here's how AI Agents differ from traditional LLMs:
 
LLMs:
Work as single units focused mainly on generating text
Process inputs and create outputs without structured decision paths
Don't have clear ways to learn from their results
 
AI Agents:
Function as multi-part systems with specialized modules for different thinking tasks
Include clear feedback paths linking results back to reasoning
Use outside tools through purpose-built connection points
 
Understanding these distinctions helps when building systems that can handle complex tasks with less human input.
 
AI Agents aren't just different; they're more advanced systems:
 
✅ Process information through purpose-built thinking
✅ Learn constantly from their results
✅ Change strategies based on what worked before
 
The feedback loop design matters. It turns one-time interactions into ongoing learning relationships, creating systems that actually get better with time.
 
Over to you: What tasks do you think would benefit the most for AI Agents?

AI Agent Architecture

The diagram below illustrates the core architecture of AI agents.

Step 1: Perception
The agent processes inputs from its environment through multiple channels. It handles language through NLP, visual data through computer vision, and contextual information to build situational awareness. Modern systems incorporate audio processing, sensor data, and state tracking to maintain a complete picture of their surroundings.

Step 2: Reasoning
At its core, the agent uses logical inference systems paired with knowledge bases to understand and interpret information. This combines symbolic reasoning, neural processing, and Bayesian approaches to handle uncertainty. The reasoning engine applies deductive and inductive processes to form conclusions and even supports creative thinking for novel solutions.

Step 3: Planning
Strategic decision-making happens through goal setting, strategy formulation, and path optimization. The agent breaks complex objectives into manageable tasks, creates hierarchical plans, and continuously optimizes to find the most efficient approach. This includes sequential planning, tactical adjustments, and simulations to test potential outcomes.

Step 4: Execution
This layer mold plans into actions through intelligent selection, tool integration, and continuous monitoring. The agent leverages APIs, code execution, web access, and specialized tools to accomplish tasks. Advanced systems support parallel and distributed execution, with implementations extending to cloud infrastructure and edge computing.

Step 5: Learning
The adaptive intelligence component combines short-term memory for immediate tasks with long-term storage for persistent knowledge. This system incorporates feedback mechanisms, using supervised, unsupervised, and reinforcement learning to improve over time. Analytics, model management, and meta-learning capabilities enable continuous enhancement.

Step 6: Interaction
The communication layer handles all external exchanges through interfaces, integration points, and output systems. This spans text, voice, and visual communication channels, with specialized components for human-AI collaboration. The agent selects appropriate formats and delivery methods based on the context.

What makes AI agent different from automation and workflows is the feedback loops between components. When execution results feed into learning systems, which then enhance reasoning capabilities, the agent achieves truly adaptive intelligence that improves with experience.

In your view: Which component has the biggest gap between theory and practice?

Most people will satisfice AI take over in 2026.

A few will be the ones building it.

10 skills that separate the two:

1. Prompt Engineering
Stop getting generic AI outputs. Learn to write prompts that make AI reason, not just respond. This is the foundation everything else builds on.

2. AI Agents
Automate entire workflows end-to-end. Not just single tasks — full processes that run while you sleep.

3. Workflow Automation
Connect your apps. Kill repetitive work. One automation can save 10+ hours a week.

4. AI Coding Assistants
Ship code without being a developer. Cursor, Codex, Claude Code — pick one and start building.

5. AI App Builders
Launch MVPs in hours, not months. Tools like Emergent, Lovable, and Replit let you go from idea to product in a single afternoon.

6. RAG (Retrieval-Augmented Generation)
Make AI accurate with your own data. No more hallucinations. No more generic answers.

7. AEO/GEO
Show up when AI searches for answers. SEO is evolving. If you're not optimizing for AI search, you're invisible.

8. AI Tool Stacking
Stop using tools in isolation. Layer them into one system that multiplies your output.

9. AI Content Generation
Scale content without scaling headcount. One person can now do what used to take a team of five.

10. LLM Ops
Track cost, accuracy, and actual ROI. If you can't measure it, you can't improve it.

The barrier to building just disappeared.

You don't need a CS degree.
You don't need to raise funding.
You don't need permission.

You just need to start.

2026 rewards builders. Not watchers.

Over to you: Which skill are you learning first?

Agentic Architectures are the hottest thing under the sun right now.

And you're still confused which Agentic Architecture to choose?

Simply put, Agentic Architectures are a 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘇𝗲𝗱 𝘄𝗮𝘆 𝗳𝗼𝗿 𝗟𝗟𝗠𝘀 𝘁𝗼 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗲 via a 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗽𝗮𝘁𝘁𝗲𝗿𝗻 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵.

What this means for 𝘺𝘰𝘶:
You can now build a 𝗳𝘂𝗹𝗹𝘆 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲𝗱 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺 using multiple agents organized in patterns that fit your specific needs.

Agentic Architectures are organizational frameworks, allowing AI systems to efficiently distribute workloads, share knowledge, and combine specialized capabilities. No more one-size-fits-all approaches!

Let's understand first Single Agent System and Multi-Agent System. Each approach has distinct advantages:

Single-Agent System:
- 𝗦𝗶𝗺𝗽𝗹𝗲𝗿 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 with one AI agent connecting directly to tools & memory
- 𝗟𝗼𝘄𝗲𝗿 𝗹𝗮𝘁𝗲𝗻𝗰𝘆 without inter-agent communication overhead
- 𝗘𝗮𝘀𝗶𝗲𝗿 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 with fewer components to integrate
- 𝗜𝗱𝗲𝗮𝗹 𝗳𝗼𝗿 focused, domain-specific tasks with clear boundaries

Multi-Agent System:
- 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 across specialized AI agents
- 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 that can grow with complexity
- 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 for improved performance on complex tasks
- 𝗜𝗱𝗲𝗮𝗹 𝗳𝗼𝗿 cross-domain problems requiring multiple types of expertise

Multi-agent systems are more implementable as they allow for custom architectures, distributed workloads, tailored precisely to your problem's complexity.

Multi-Agent System Patterns
1️⃣ Parallel: Multiple agents process simultaneously for maximum speed and throughput.

2️⃣ Sequential: Agents work in sequence, each refining previous outputs for complex tasks.

3️⃣ Loop: Circular flow enables iterative improvement until desired quality is reached.

4️⃣ Router: One agent directs inputs to specialized paths based on content analysis.

5️⃣ Aggregator: Consolidates multiple inputs into comprehensive unified outputs.

6️⃣ Network: Interconnected agents share knowledge bidirectionally for complex reasoning.

7️⃣ Hierarchical: Manager-worker structure handles complexity through delegated subtasks.

Multi-agent systems win because you can mix-and-match patterns to solve exactly your problem.

Agentic Architecture examples:
1️⃣ Hierarchical: Parent-child agent delegation with clear authority flows

2️⃣ Human-in-the-loop: AI systems with human oversight at critical points

3️⃣ Shared tools: Multiple agents accessing common resources efficiently

4️⃣ Sequential: Agents working in chain order, each building on previous outputs

5️⃣ Database with tools: Centralized knowledge with specialized access methods

6️⃣ Memory transformation using tool: Raw data conversion into structured AI memory

Over to you: Which agentic architecture pattern you like?

AI agents without proper memory are just expensive chatbots repeating the same mistakes.

After building 50+ production agents, I discovered most developers only implement 1 out of 5 critical memory types.

Here's the complete memory architecture powering agents at Google, Microsoft, and top AI startups:

𝗦𝗵𝗼𝗿𝘁-𝘁𝗲𝗿𝗺 𝗠𝗲𝗺𝗼𝗿𝘆 (𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝗠𝗲𝗺𝗼𝗿𝘆)
→ Maintains conversation context (last 5-10 turns)
→ Enables coherent multi-turn dialogues
→ Clears after session ends
→ Implementation: Rolling buffer/context window

𝗟𝗼𝗻𝗴-𝘁𝗲𝗿𝗺 𝗠𝗲𝗺𝗼𝗿𝘆 (𝗣𝗲𝗿𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗦𝘁𝗼𝗿𝗮𝗴𝗲)
Unlike short-term memory, long-term memory persists across sessions and contains three specialized subsystems:

𝟭. 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗠𝗲𝗺𝗼𝗿𝘆 (𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗕𝗮𝘀𝗲)
→ Domain expertise and factual knowledge
→ Company policies, product catalogs
→ Doesn't change per user interaction
→ Implementation: Vector DB (Pinecone/Qdrant) + RAG

𝟮. 𝗘𝗽𝗶𝘀𝗼𝗱𝗶𝗰 𝗠𝗲𝗺𝗼𝗿𝘆 (𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗟𝗼𝗴𝘀)
→ Specific past interactions and outcomes
→ "Last time user tried X, Y happened"
→ Enables learning from past actions
→ Implementation: Few-shot prompting + event logs

𝟯. 𝗣𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗮𝗹 𝗠𝗲𝗺𝗼𝗿𝘆 (𝗦𝗸𝗶𝗹𝗹 𝗦𝗲𝘁𝘀)
→ How to execute specific workflows
→ Learned task sequences and patterns
→ Improves with repetition
→ Implementation: Function definitions + prompt templates

When processing user input, intelligent agents don't query memories in isolation:
1️⃣ Short-term provides immediate context
2️⃣ Semantic supplies relevant domain knowledge
3️⃣ Episodic recalls similar past scenarios
4️⃣ Procedural suggests proven action sequences

This orchestrated approach enables agents to:
- Handle complex multi-step tasks autonomously
- Learn from failures without retraining
- Provide contextually aware responses
- Build relationships over time

LangChain, LangGraph, and AutoGen all provide memory abstractions, but most developers only scratch the surface.

The difference between a demo and production? Memory that actually remembers.

Over to you: Which memory type is your agent missing?