Google’s AI Agents & DeepSeek Explained in 5 Mins

Skip the long research papers—here’s a concise, no-fluff breakdown of Google’s AI Agents, how they work, and why DeepSeek-R1 is making waves in AI reasoning.

AI is no longer just about language models answering questions—it’s about AI acting. But why now? And what makes AI agents different from the large language models (LLMs) we’ve been using?

This week, I break down two of the most talked-about advancements in AI right now:

• AI Agents – Why they’re creating a buzz in the AI and product community.
• DeepSeek-R1 – A reinforcement learning-based approach that claims to push AI reasoning to new heights.

Let’s dive in.

Hi, I'm Snigdha! I'm on a journey to become a 1% better Product Manager every day, and I’m excited to share my learnings with you. My goal is to provide bite-sized insights and practical tips for those exploring the world of product management, helping you grow your PM skills one small step at a time.

AI Agents - General Concept

AI agents operate using three fundamental layers:

1. Model: The AI brain (e.g., GPT-4, Gemini, Claude) that generates outputs.
2. Tools & Functions: External integrations that allow the agent to interact with databases, APIs, and the real world.
3. Orchestration Layer: A control system that helps the agent govern reasoning, memory, and decision-making, using frameworks like ReAct, Chain-of-Thought (CoT), and Tree-of-Thoughts (ToT).

But how does this actually work?

What Makes AI Agents Work?

The Logic of ReACT (Reasoning + Acting)

The ReACT framework is at the core of how modern AI agents function. Instead of just responding, the agent iterates through three key cycles:

1. Observation → Think: The agent processes the current state, recalling past memory if needed.
2. Plan → Decide: The agent generates Chain-of-Thought (CoT) reasoning and picks the next best action.
3. Act → Reflect: The agent executes an action (API call, function invocation, tool usage, or next reasoning step), then reevaluates the outcome for the next iteration.

Example of ReACT in action: Let’s say an AI agent is tasked with booking a flight.

• Step 1: Observes Input → User asks for a cheap flight to Tokyo.
• Step 2: Plans Next Steps → The agent breaks it down: "I need to check Skyscanner first, then compare it to Google Flights."
• Step 3: Executes the API Calls → The agent queries Skyscanner and fetches results.
• Step 4: Reflects on Results → If the price is higher than expected, the agent tries different dates to optimize for a better deal. The agent evaluates different alternatives. “I see cheaper flight tickets on Google Flights, I will recommend this option.”
• Step 5: Continues Until Completion → The agent either books the flight or asks the user for more information.

This multi-step, adaptive execution model is what makes AI agents different from traditional LLMs, which just return a single response and stop.

The Role of Tools in AI Agents

AI Agents integrate web APIs, retrieval-augmented generation (RAG), and function calling to expand their capabilities.

Example: An AI agent in customer support could fetch live data on a user’s last purchase from a CRM instead of relying on pre-trained responses.

The Role of Vector Search & RAG for Real-Time Knowledge Retrieval

Instead of relying solely on pre-trained data, AI agents leverage retrieval-augmented generation (RAG) to fetch relevant real-time data:

1. Convert a user query into an embedding (numerical representation of text, images, or other data).
2. Match it with stored data in a vector database (stores and manages high-dimensional embeddings), example, SCaNN for nearest-neighbor search.
3. Retrieve the most relevant knowledge.
4. Provide a response informed by up-to-date data

Example RAG in action: Imagine an AI-powered legal assistant. When a lawyer asks, "What are the key clauses in a standard NDA?", the system:

1. Embeds the query into a numerical representation.
2. Uses ScaNN to search the vector database of legal documents.
3. Finds the most relevant NDA clauses based on similarity scores.
4. Retrieves & summarizes them in a natural language response (an AI agent can also perform successive actions.

Comparison Between Agents and Standard LLMs

I built a quick comparison guide for you to see the stark differences between AI Agents and LLM models.

Now let’s shift some gears and quickly learn what is making DeepSeek R1 LLM different than existing LLMs.

DeepSeek-R1: The Next Leap in AI Reasoning

What’s Special About DeepSeek-R1?

DeepSeek-R1 is not your typical large language model (LLM). Unlike traditional models that rely heavily on supervised fine-tuning (SFT), DeepSeek-R1 pioneers an RL-first approach—meaning it learns reasoning capabilities purely through Reinforcement Learning (RL) without pre-training on human-labeled datasets.

This is a big deal because:

• It shows that LLMs can evolve reasoning skills autonomously without massive human-labeled data.
• It achieves performance comparable to OpenAI-o1-1217, setting new benchmarks in math, logic, and coding tasks.
• It introduces a multi-stage training pipeline that blends RL and rejection sampling to fine-tune reasoning ability iteratively.

How Does It Work?

[Unlock the article and read the full issue on Substack or Medium for free!]

AI is shifting from being reactive to being proactive.

Past Articles

• 2024: The Year of Risks, Resilience, and Results? (Substack, Medium)
• Taylor Swift - The 'Mastermind' Product Manager (Substack, Medium)
• Notion: Swiss Army knife of workspace solutions (Substack, Medium)
• Navigating the Tough PM Job Market: Lessons from Yash Somani's Journey (Substack, Medium)
• Tired of Vague AI Advice? Here’s a Practical Roadmap to Becoming an AI PM (Substack, Medium)
• Have LLMs Killed Grammarly? (Substack, Medium)
• The Innovation Playbook: How Amplitude Reinvents Itself to Stay Ahead (Substack, Medium)
• Inside Walmart’s AI Strategy: A CPO’s Secrets to Retail Success (Substack, Medium)

If you enjoy this sneak peek, subscribe and join 100+ readers on Substack and Medium!

Schedule:

• Tuesdays: Get a sneak peak of last week's issue on LinkedIn
• Thursdays: Free new releases of Become 1% Better PM articles on Substack and Medium!

Let’s continue this journey of learning and growing together. Stay tuned for more insights, and thank you for joining me to Become 1% Better PM every week! 🚀

#Become1BetterPM #ProductManagement #Learning #Growth #CareerPivot #Product