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The Genesis and Evolution of AI Agents (Chapter 1)
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Chapter 1 from the book "Agentic AI: Theories and Practices" (By Ken Huang)
A conversational deep-dive into how AI went from tools to thinking collaborators
Introduction: Why Everyone Is Suddenly Talking About “AI Agents”
If you’ve been following AI over the last few years, you’ve probably noticed something interesting. The conversation has shifted.
We’re no longer just talking about:
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chatbots that answer questions,
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models that summarize text,
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or systems that classify images.
Instead, we’re hearing phrases like:
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AI agents
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autonomous systems
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AI coworkers
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long-horizon reasoning
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AI that plans and acts
This chapter opens by saying, very clearly: we are entering a new era of AI. Not just better AI—but different AI.
AI Agents are not just smarter tools. They are systems that can:
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decide what to do,
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figure out how to do it,
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act on their own,
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and improve over time.
That might sound like science fiction, but the chapter’s core argument is simple: the foundations for this shift already exist, and we’re watching it happen in real time.
What Is an AI Agent? (And Why It’s Hard to Define)
Why the Definition Is Slippery
The chapter is refreshingly honest: there is no single, perfect definition of an AI Agent.
Why? Because:
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the technology is evolving fast,
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new capabilities keep getting added,
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and today’s “advanced” system becomes tomorrow’s baseline.
Still, the author gives us a good enough definition so we know what we’re talking about.
The Big Idea: From Passive Software to Active Entities
Traditional software is passive. You click a button, it responds. You give it instructions, it executes them exactly as written.
AI Agents are different.
At their core, AI Agents are digital entities that can perceive, think, and act with a degree of independence.
Instead of waiting to be told every step, they can:
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explore information on their own,
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decide which path to take,
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plan multiple steps ahead,
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adjust behavior based on feedback.
In other words, they behave less like calculators and more like junior collaborators.
What Makes an AI Agent Different from Old AI?
The chapter emphasizes that this isn’t just an incremental upgrade. It’s a qualitative shift.
Earlier AI systems were:
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rule-bound,
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narrow,
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brittle,
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and static.
AI Agents are:
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adaptive,
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proactive,
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flexible,
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and capable of handling messy, real-world situations.
They don’t just answer questions. They pursue goals.
Core Characteristics of AI Agents (Explained Simply)
The chapter then breaks down what actually makes something an AI Agent. Let’s go through these traits in plain language.
1. Autonomy and Initiative
This is the big one.
An AI Agent doesn’t need a human to micromanage every step. Once given a goal, it can:
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decide what actions are needed,
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evaluate different options,
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choose the best path forward.
This autonomy is usually powered by decision-making algorithms and reinforcement learning, but you don’t need to know the math to get the idea.
Think of the difference between:
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a GPS that only gives directions when you ask, and
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a navigation system that reroutes automatically when traffic changes.
That second one feels more agent-like.
2. Adaptability and Learning
AI Agents don’t just follow instructions—they learn from experience.
Using techniques like deep learning and transfer learning, they can:
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improve with new data,
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adapt to unfamiliar situations,
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apply past knowledge in new contexts.
This is closer to how humans learn. We don’t relearn everything from scratch every time—we generalize.
3. Multimodal Perception
Humans don’t just read text—we see, hear, and sense the world.
Modern AI Agents are moving in the same direction. They can process:
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text and speech,
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images and video,
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sensor data,
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sometimes even radar or infrared signals.
By combining multiple input types, agents build a richer understanding of their environment, which leads to better decisions.
4. Reasoning and Problem-Solving
This is where things get really interesting.
AI Agents don’t just retrieve information. They can:
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reason step by step,
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infer causes and effects,
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break complex problems into manageable parts.
They often combine:
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symbolic logic (rules and structure),
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probabilistic reasoning (handling uncertainty),
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neural networks (pattern recognition).
That hybrid approach lets them tackle problems that require more than brute-force computation.
5. Social Intelligence and Collaboration
AI Agents aren’t designed to work alone.
Advanced agents can:
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hold conversations,
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understand intent and emotion,
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collaborate with humans,
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coordinate with other AI agents.
This is crucial for real-world deployment, where problems are rarely solved by a single isolated system.
6. Ethical Reasoning and Value Alignment
As AI Agents gain autonomy, ethics becomes unavoidable.
The chapter stresses that agents must:
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reason about consequences,
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align with human values,
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respect social norms.
This is an active research area, and it’s not “solved.” But it’s central to responsible deployment.
7. Meta-Learning: Learning How to Learn
At the frontier is meta-learning—agents that improve not just their skills, but their learning process itself.
This means:
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faster adaptation,
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less retraining,
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more independence from human engineers.
8. Explainability and Transparency
As agents grow more complex, humans need to understand:
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why an agent made a decision,
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how it arrived at a conclusion.
Explainability builds trust and accountability—especially in high-stakes domains like healthcare or finance.
9. Domain Agnosticism
Earlier AI systems were specialists. Modern AI Agents aim to be generalists.
They can:
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transfer knowledge across domains,
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apply skills learned in one area to another.
This is a major step toward more flexible, human-like intelligence.
10. Embodied Intelligence
AI Agents aren’t just software.
When combined with robotics and IoT, they can:
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move in the physical world,
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interact with objects,
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operate vehicles,
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assist in manufacturing and healthcare.
This bridges digital intelligence with physical action.
A Brief History: How AI Agents Came to Be
To understand why today’s agents feel revolutionary, the chapter walks us through history.
The Dartmouth Conference (1956): Where AI Was Born
The term “Artificial Intelligence” was coined at the Dartmouth Conference in 1956.
The goal was bold: to make machines simulate aspects of human intelligence.
At the time, progress was slow due to:
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limited hardware,
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lack of data,
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overly ambitious expectations.
Still, this conference planted the seed.
1970s–1980s: Expert Systems
This era produced expert systems—programs that encoded human knowledge as rules.
MYCIN, for example, helped diagnose bacterial infections.
Expert systems worked well in narrow domains but:
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couldn’t adapt,
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couldn’t learn,
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broke outside predefined scenarios.
The Actor Model: Early Agent Thinking
Carl Hewitt’s Actor Model proposed systems made of independent “actors” that communicate via messages.
This idea strongly resembles modern:
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multi-agent systems,
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distributed AI architectures.
You can see echoes of it today in frameworks like AutoGen and LangGraph.
1990s: Software Agents and the Internet
As the internet grew, researchers like Pattie Maes explored software agents that acted on behalf of users.
Her work influenced:
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recommendation systems,
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personalization,
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intelligent user interfaces.
Many everyday features—like online recommendations—trace back to this era.
2000s: Machine Learning Joins the Party
Reinforcement learning became central.
Agents could now:
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take actions,
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receive feedback,
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improve over time.
Projects like DARPA’s CALO laid the groundwork for assistants like Siri.
2010s–Present: The AI Agent Renaissance
This is where everything accelerated.
Key breakthroughs:
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deep learning,
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GPUs,
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transformers,
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large language models.
Models like GPT-4, Claude, and Gemini unlocked:
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natural language understanding,
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reasoning,
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planning,
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tool use.
According to OpenAI’s framework, AI progress now spans five levels—from narrow chatbots to fully autonomous organizational agents.
Taxonomy of AI Agents: Different Flavors of Intelligence
The chapter then introduces a taxonomy—a way to categorize different types of agents.
Reactive Agents
The simplest kind.
They:
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respond quickly,
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follow stimulus-response rules,
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don’t learn or plan.
Examples:
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obstacle avoidance in robotics,
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high-frequency trading.
Fast, but shallow.
Deliberative Agents
These agents:
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build internal models,
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plan ahead,
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reason symbolically.
They’re good at strategy but:
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computationally heavy,
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slower to react,
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sensitive to model inaccuracies.
Hybrid Agents
Hybrid agents combine:
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reactive speed,
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deliberative planning.
They’re common in:
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autonomous vehicles,
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intelligent assistants.
Powerful, but architecturally complex.
Learning Agents
These agents improve with experience.
They:
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adapt to changing environments,
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generalize from past data.
Examples:
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recommendation engines,
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adaptive control systems.
Their weakness? Data dependency and potential bias.
Cognitive Agents
The most ambitious category.
They aim for:
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human-like reasoning,
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abstract thinking,
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language fluency.
Examples include advanced research assistants and creative AI systems.
Collaborative Agents
Designed to work in groups.
They:
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communicate,
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coordinate,
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solve distributed problems.
Examples:
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swarm robotics,
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multi-agent recommendation systems.
Competitive (Adversarial) Agents
These agents operate in conflict.
They:
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model opponents,
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use game theory,
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anticipate adversarial actions.
Examples:
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cybersecurity,
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trading bots,
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competitive games.
Vertical or Domain-Specific Agents
These are specialists.
They:
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excel in one domain,
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outperform general systems there,
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don’t generalize well.
Examples:
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medical diagnosis systems,
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chess engines,
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financial trading algorithms.
Expertise comes at the cost of flexibility.
What Enabled the AI Agent Renaissance?
The chapter highlights a “perfect storm” of technologies.
1. Massive Compute Power
GPUs, TPUs, and specialized chips removed earlier limits.
2. Advances in Natural Language Processing
Large language models bridged the gap between human language and machine reasoning.
3. The Data Explosion
Big data and IoT provided endless learning material.
4. Algorithmic Innovation
Reinforcement learning, transformers, and self-supervised learning pushed boundaries.
5. Interdisciplinary Convergence
Insights from neuroscience, psychology, and computer science shaped modern agent design.
Real-World Examples: Operator and STaR
OpenAI’s Operator Agent
Operator is designed to:
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navigate the internet autonomously,
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conduct deep research,
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handle long-horizon tasks,
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reason step by step using chain-of-thought.
It can run multiple agents in parallel and shows early signs of PhD-level reasoning.
Stanford’s Self-Taught Reasoner (STaR)
STaR focuses on self-improvement.
It:
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generates its own training data,
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learns from limited examples,
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applies reasoning across domains,
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uses chain-of-thought for transparency.
This shows how agents might learn more like humans—through reflection and iteration.
Why This Chapter Matters
The chapter closes with a powerful message:
AI Agents are not just a technological upgrade. They are a paradigm shift.
They move AI from:
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tools → collaborators,
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narrow → general,
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reactive → proactive.
They will reshape:
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work,
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organizations,
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creativity,
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problem-solving.
And they force us to think carefully about ethics, responsibility, and human–AI partnership.
Final Takeaway
AI Agents are not magic.
They are not conscious.
They are not replacements for humans.
But they are the most significant change in how software behaves since the invention of computing.
We’re not just building smarter machines—we’re building systems that think, act, and learn alongside us.
And this chapter is your roadmap to understanding how we got here.
AI Engineering Architecture and User Feedback (Chapter 10 - AI Engineering - By Chip Huyen)
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Inference Optimization: How AI Models Become Faster, Cheaper, and Actually Useful (Chapter 9 - AI Engineering - By Chip Huyen)
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Why “running” an AI model well is just as hard as building it
Introduction: Why Inference Matters More Than You Think
In the AI world, we spend a lot of time talking about training models—bigger models, better architectures, more data, and more GPUs. But once a model is trained, a much more practical question takes over:
How do you run it efficiently for real users?
This is where inference optimization comes in.
Inference is the process of using a trained model to produce outputs for new inputs. In simple terms:
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Training is learning
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Inference is doing the work
No matter how brilliant a model is, if it’s too slow, users will abandon it. If it’s too expensive, businesses won’t deploy it. Worse, if inference takes longer than the value of the prediction, the model becomes useless—imagine a stock prediction that arrives after the market closes.
As the chapter makes clear, inference optimization is about making AI models faster and cheaper without ruining their quality. And it’s not just a single discipline—it sits at the intersection of:
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Machine learning
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Systems engineering
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Hardware architecture
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Compilers
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Distributed systems
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Cloud infrastructure
This blog post explains inference optimization in plain language, without skipping the important details, so you can understand why AI systems behave the way they do—and how teams improve them in production .
1. Understanding Inference: From Model to User
Training vs Inference (A Crucial Distinction)
Every AI model has two distinct life phases:
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Training – learning patterns from data
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Inference – generating outputs for new inputs
Training is expensive but happens infrequently. Inference happens constantly.
Most AI engineers, application developers, and product teams spend far more time worrying about inference than training, especially if they’re using pretrained or open-source models.
What Is an Inference Service?
In production, inference doesn’t happen in isolation. It’s handled by an inference service, which includes:
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An inference server that runs the model
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Routing logic
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Request handling
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Possibly preprocessing and postprocessing
Model APIs like OpenAI, Gemini, or Claude are inference services. If you use them, you don’t worry about optimization details. But if you host your own models, you own the entire inference pipeline—performance, cost, scaling, and failures included .
Why Optimization Is About Bottlenecks
Optimization always starts with a question:
What is slowing us down?
Just like traffic congestion in a city, inference systems have chokepoints. Identifying these bottlenecks determines which optimization techniques actually help.
2. Compute-Bound vs Memory-Bound: The Core Bottleneck Concept
Two Fundamental Bottlenecks
Inference workloads usually fall into one of two categories:
Compute-bound
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Speed limited by how many calculations the hardware can perform
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Example: heavy mathematical computation
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Faster chips or more parallelism help
Memory bandwidth-bound
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Speed limited by how fast data can be moved
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Common in large models where weights must be repeatedly loaded
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Faster memory and smaller models help
This distinction is foundational to understanding inference optimization .
Why Language Models Are Often Memory-Bound
Large language models generate text one token at a time. For each token:
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The model must load large weight matrices
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Perform relatively little computation per byte loaded
This makes decoding (token generation) memory bandwidth-bound.
Prefill vs Decode: Two Very Different Phases
Transformer-based language model inference has two stages:
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Prefill
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Processes input tokens in parallel
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Compute-bound
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Determines how fast the model “understands” the prompt
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Decode
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Generates one output token at a time
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Memory bandwidth-bound
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Determines how fast the response appears
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Because these phases behave differently, modern inference systems often separate them across machines for better efficiency .
3. Online vs Batch Inference: Latency vs Cost
Two Types of Inference APIs
Most providers offer:
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Online APIs – optimized for low latency
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Batch APIs – optimized for cost and throughput
Online Inference
Used for:
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Chatbots
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Code assistants
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Real-time interactions
Characteristics:
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Low latency
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Users expect instant feedback
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Limited batching
Streaming responses (token-by-token) reduce perceived waiting time but come with risks—users might see bad outputs before they can be filtered.
Batch Inference
Used for:
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Synthetic data generation
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Periodic reports
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Document processing
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Data migration
Characteristics:
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Higher latency allowed
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Aggressive batching
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Much lower cost (often ~50% cheaper)
Unlike traditional ML, batch inference for foundation models can’t precompute everything because user prompts are open-ended .
4. Measuring Inference Performance: Metrics That Actually Matter
Latency Is Not One Number
Latency is best understood as multiple metrics:
Time to First Token (TTFT)
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How long users wait before seeing anything
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Tied to prefill
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Critical for chat interfaces
Time Per Output Token (TPOT)
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Speed of token generation after the first token
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Determines how fast long responses feel
Two systems with the same total latency can feel very different depending on TTFT and TPOT trade-offs.
Percentiles, Not Averages
Latency is a distribution.
A single slow request can ruin the average. That’s why teams look at:
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p50 (median)
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p90
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p95
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p99
Outliers often signal:
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Network issues
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Oversized prompts
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Resource contention .
Throughput and Cost
Throughput measures how much work the system does:
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Tokens per second (TPS)
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Requests per minute (RPM)
Higher throughput usually means lower cost—but pushing throughput too hard can destroy user experience.
Goodput: Throughput That Actually Counts
Goodput measures how many requests meet your service-level objectives (SLOs).
If your system completes 100 requests/minute but only 30 meet latency targets, your goodput is 30, not 100.
This metric prevents teams from optimizing the wrong thing.
5. Hardware: Why GPUs, Memory, and Power Dominate Costs
What Is an AI Accelerator?
An accelerator is specialized hardware designed for specific workloads.
For AI, the dominant accelerator is the GPU, designed for massive parallelism—perfect for matrix multiplication, which dominates neural network workloads.
Why GPUs Beat CPUs for AI
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CPUs: few powerful cores, good for sequential logic
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GPUs: thousands of small cores, great for parallel math
More than 90% of neural network computation boils down to matrix multiplication, which GPUs excel at .
Memory Hierarchy Matters More Than You Think
Modern accelerators use multiple memory layers:
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CPU DRAM (slow, large)
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GPU HBM (fast, smaller)
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On-chip SRAM (extremely fast, tiny)
Inference optimization is often about moving data less and smarter across this hierarchy.
Power Is a Hidden Bottleneck
High-end GPUs consume enormous energy:
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An H100 running continuously can use ~7,000 kWh/year
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Comparable to a household’s annual electricity use
This makes power—and cooling—a real constraint on AI scaling .
6. Model-Level Optimization: Making Models Lighter and Faster
Model Compression Techniques
Several techniques reduce model size:
Quantization
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Reduce numerical precision (FP32 → FP16 → INT8 → INT4)
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Smaller models
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Faster inference
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Lower memory bandwidth use
Distillation
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Train a smaller model to mimic a larger one
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Often surprisingly effective
Pruning
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Remove unimportant parameters
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Creates sparse models
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Less common due to hardware limitations
Among these, weight-only quantization is the most widely used in practice .
The Autoregressive Bottleneck
Generating text one token at a time is:
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Slow
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Expensive
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Memory-bandwidth heavy
Several techniques attempt to overcome this fundamental limitation.
Speculative Decoding
Idea:
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Use a smaller “draft” model to guess future tokens
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Have the main model verify them in parallel
If many draft tokens are accepted, the system generates multiple tokens per step—dramatically improving speed without hurting quality.
This technique is now widely supported in modern inference frameworks.
Inference with Reference
Instead of generating text the model already knows (e.g., copied context), simply reuse tokens from the input.
This works especially well for:
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Document Q&A
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Code editing
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Multi-turn conversations
It avoids redundant computation and speeds up generation.
Parallel Decoding
Some techniques try to generate multiple future tokens simultaneously.
Examples:
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Lookahead decoding
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Medusa
These approaches are promising but complex, requiring careful verification to ensure coherence.
7. Attention Optimization: Taming the KV Cache Explosion
Why Attention Is Expensive
Each new token attends to all previous tokens.
Without optimization:
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Computation grows quadratically
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KV cache grows linearly—but still huge
For large models and long contexts, the KV cache can exceed model size itself .
KV Cache Optimization Techniques
Three broad strategies exist:
Redesigning Attention
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Local window attention
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Multi-query attention
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Grouped-query attention
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Cross-layer attention
These reduce how much data must be stored and reused.
Optimizing KV Cache Storage
Frameworks like vLLM introduced:
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PagedAttention
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Flexible memory allocation
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Reduced fragmentation
Other approaches:
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KV cache quantization
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Adaptive compression
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Selective caching
Writing Better Kernels
Instead of changing algorithms, optimize how computations are executed on hardware.
The most famous example:
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FlashAttention
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Fuses multiple operations
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Minimizes memory access
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Huge speedups in practice
8. Service-Level Optimization: Making the Whole System Work
Batching: The Simplest Cost Saver
Batching combines multiple requests:
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Improves throughput
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Reduces cost
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Increases latency
Types:
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Static batching
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Dynamic batching
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Continuous batching
The trick is batching without hurting latency too much.
Compilers and Kernels
Modern inference relies heavily on:
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Compilers (torch.compile, XLA, TensorRT)
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Hardware-specific kernels
These translate high-level model code into highly optimized machine instructions.
Many companies treat their kernels as trade secrets because they directly translate into cost advantages .
Conclusion: Inference Optimization Is the Real Production Skill
Training models gets headlines. Inference optimization keeps businesses alive.
Inference optimization:
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Determines user experience
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Dominates long-term cost
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Requires cross-disciplinary thinking
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Is where real-world AI engineering happens
As models become commoditized, efficient inference becomes a competitive moat.
The future of AI won’t be decided by who trains the biggest model—but by who can run models fastest, cheapest, and most reliably.
Addendum
My prompt:Role: You an expert in AI Engineering and a prolific writer. Task: Spin this attached chapter 9 as a blog post in layman terms Rules: Organize the post in 7-8 sections with subsections as needed Note: Blog post should be about 6000 to 7000 words long Note: Try not to miss any important section or detailsTags: Artificial Intelligence,Generative AI,Agentic AI,Technology,Book Summary,