HOW TO BUILD IN THE AI AGENT ERA

AI is moving from answering prompts to completing complex work.

The opportunity is no longer just building a better model. It is designing the system around it: tools, context, memory, evaluators, hardware, and clear objectives.

THE NEW AI REALITY

AI HAS REACHED JUNIOR ENGINEER LEVEL

Jeff Dean believes modern coding agents are now capable enough to match many definitions of a junior engineer.

Their progress is also moving faster than expected. The next advantage comes from giving them longer tasks, better tools, and clearer operating rules.

AGENTS WILL WORK FOR DAYS

Most people still imagine an AI task lasting minutes or hours.

The bigger shift is agents that can work for days or weeks: rebuilding software in another language, testing alternatives, improving performance, and completing projects that once required an entire team.

THE NEXT LEAP: AI IMPROVING AI

By 2027, more ML systems may improve themselves through automated loops.

The system breaks a large problem into subproblems, runs many experiments, evaluates the results, combines the strongest solutions, and produces a better system with less human intervention.

INFERENCE IS THE NEW BOTTLENECK

THE PRODUCT CHANGES WHEN WAITING DISAPPEARS

Agent products feel limited when every step requires waiting.

Jeff Dean asks founders to imagine inference with dramatically lower latency. A system that responds 50× faster would not feel like a chatbot. It could operate continuously, interact naturally, and execute in real time.

AI PROBLEMS ARE OFTEN SYSTEMS PROBLEMS

Many teams call a limitation a model problem when the real bottleneck is hardware, memory bandwidth, data movement, or network communication.

Understanding the whole stack helps founders avoid expensive model changes when the better answer is a faster system design.

MOVING DATA COSTS MORE THAN COMPUTING

A calculation can be cheap. Moving the data required for that calculation can consume roughly 1,000× more energy.

This is why AI systems use batching. They process many tokens together to spread the data-movement cost, even though batching can reduce responsiveness.

INFERENCE NEEDS SPECIALIZED HARDWARE

Training can tolerate delay. Inference cannot.

Future hardware may minimize data movement, use extremely low-precision operations, and support fewer unnecessary features. The goal is not a machine that does everything. It is a machine that serves intelligence quickly and efficiently.

CONTEXT ENGINEERING BECOMES THE PRODUCT

THE MODEL IS ONLY ONE COMPONENT

A powerful AI product combines a model with retrieval, memory, tools, workflows, and evaluation.

The model must know what information to fetch, which tool to use, how to divide the task, when to try another approach, and how to judge whether the result is good.

CONTEXT IS CLEARER THAN TRAINING DATA

Training data is compressed into billions of parameters. The model does not recall every detail clearly.

Information placed directly in the current context is easier to use. This gives small teams a major advantage: they can improve results without training a frontier model.

TURN HUMAN METHODS INTO AI SKILLS

Jeff Dean and Sanjay Ghemawat encoded their performance-optimization process as an AI skill.

The agent learned to run benchmarks, change code, measure improvements, inspect trade-offs, and iterate. The core method was simple: give the model the workflow an expert would follow.

IMPROVE THE SYSTEM BY STUDYING FAILURES

Use agents on real problems and watch where they fail.

The fix may be a better instruction, a missing example, a new tool, a clearer evaluation rule, or a reusable skill. Context engineering improves when every failure becomes a permanent upgrade to the system.

RELIABLE AGENTS NEED STRUCTURE

AGENTS DRIFT OUTSIDE THEIR COMFORT ZONE

Agents often work well for the first few steps, then become less reliable.

As the task moves beyond patterns the model knows, performance can fall quickly. Long-running systems need guidance that keeps the agent on a clear, well-lit path.

USE MULTIPLE AGENTS TO SEARCH SOLUTIONS

A stronger system can send several agents to try different approaches.

Another agent or evaluator compares the results, keeps promising paths, and discards weak ones. This turns inference into a search process and makes complex workflows more reliable.

SKILLS KEEP AGENTS INSIDE THE WORKFLOW

A general model may not know your internal software, process, or quality standard.

Skills can teach it how to fetch logs, review code, run tests, access data, follow approval rules, and complete work in the correct order. This is how a general model becomes useful inside a specific company.

WHERE SMALL STARTUPS CAN WIN

WIN THROUGH DOMAIN DEPTH

Large AI companies build systems that work across many domains.

A small team can win by focusing on one painful workflow and designing a better interface, better data, better skills, stronger evaluation, and higher accuracy for that exact user.

LOOK FOR 0–1% MODEL SUCCESS

Test frontier models on the problem before building.

If they already succeed 20% of the time, the capability may improve quickly and absorb your product. A stronger opportunity is a task where general models currently succeed almost never.

PRIVATE DATA CREATES DEFENSIBILITY

A general model may organize public information well. It cannot automatically understand a user’s private history, company systems, customer records, or operating decisions.

Products that securely organize and activate this unique data can create durable value.

SPECIALIZED MODELS STILL MATTER

Some problems need a focused model trained for one scientific or technical domain.

AlphaFold is the pattern: a specialized system can outperform general intelligence on a narrow, valuable problem. Similar opportunities may exist in materials, chip design, biology, and other expert fields.

BECOME AN AI-NATIVE FOUNDER

CLEAR SPECS MULTIPLY AGENT OUTPUT

Agents perform better when the target is explicit.

Define the objective, inputs, constraints, expected behavior, tests, edge cases, and acceptance criteria. The clearer the specification, the less the agent must guess and the more work you can delegate safely.

EXISTING SOFTWARE IS A PERFECT SPEC

Agents are strong at translating software between programming languages because the original system is a detailed specification.

The code, behavior, and tests define the target. The agent can rebuild it, compare outputs, fix differences, and continue until the new version behaves correctly.

THE SCARCE SKILL IS TASTE

When agents can write most of the code, execution becomes abundant.

The scarce skill is choosing the right problem, defining the right product, recognizing quality, and knowing what not to build. Excellent execution on an unimportant problem still creates little value.

BUILD BETTER TASTE

RECORD WHAT YOU THINK WILL MATTER

Write down several problems or technologies you believe will become important in the next 12 months.

Later, review what actually happened. Which ideas became valuable? Which were ignored? Which assumptions were wrong? Repeated prediction and review builds practical taste.

QUESTION ASSUMPTIONS FROM FIRST PRINCIPLES

Do not stay anchored to how a problem is solved today.

Ask what would happen if speed improved 10×, cost fell 100×, errors became acceptable, or a constraint disappeared. Most thought experiments fail, but the successful ones can create new system architectures.

MAPREDUCE CAME FROM REMOVING NOISE

Google’s large-scale jobs mixed simple computation with complex code for parallelism, failures, and checkpointing.

Jeff Dean and Sanjay Ghemawat separated the repeated infrastructure from the actual task. That abstraction became MapReduce and made distributed computing easier to use.

TPU CAME FROM SIMPLE NAPKIN MATH

Better speech recognition meant people would use it more. Serving that demand on CPUs could require a dramatically larger server fleet.

That calculation exposed the bottleneck before it arrived. The response was specialized hardware that later became foundational for modern AI.

AUTOMATED SCIENCE CHANGES EVERYTHING

THE SCIENTIFIC METHOD CAN BECOME SOFTWARE

AI can automate the experimental loop: propose an idea, implement it, run the experiment, measure the result, and choose the next direction.

When thousands of experiments can run automatically, progress is measured by useful discoveries per unit of compute.

MAKE THE EVALUATOR FASTER

Some scientific simulations take hours for one answer.

Researchers can train a neural approximation using inputs and outputs from the expensive simulator. Jeff Dean described one case that became about 300,000× faster while remaining nearly as accurate.

AI WILL ORCHESTRATE SUBPROBLEMS

The most powerful systems will accept a high-level objective, divide it into subproblems, run automated experiments for each one, and combine the results.

This pattern could accelerate machine learning, science, engineering, molecule discovery, and chip design.

BUILD FOR POSITIVE IMPACT

REJECTION DOES NOT DEFINE IMPORTANCE

Jeff Dean’s work on model distillation was rejected by a conference reviewer who doubted its impact.

The idea later became central to creating smaller, faster, capable models. Good work can be early, misunderstood, or evaluated through the wrong lens.

CHOOSE WORK THAT CHANGES THE OUTCOME

Before committing years to a problem, ask one question:

If we succeed completely, will the world become meaningfully better?

A difficult project should create more than a technically impressive demo. It should unlock real capability, value, or progress.

BUILD WITH LOW-EGO PEOPLE

Choose teammates who are excellent in areas you are not, enjoy solving hard problems, and care more about the result than personal credit.

A strong team combines different tools, learns from each other, and becomes capable of building what no individual could create alone.

YOUR FOUNDER PLAYBOOK

1. Find a problem general models cannot solve.

2. Add private context, tools, skills, and evaluators.

3. Write a precise specification.

4. Run multiple agents and measurable loops.

5. Optimize the full system for speed, reliability, and impact.

THE NEXT ADVANTAGE IS SYSTEM DESIGN

The winning product will not be the one with the longest prompt.

It will combine the right problem, proprietary context, expert workflows, reliable evaluation, low-latency execution, and strong founder taste into a system users can trust.