What is Agentic AI? Which terms really matter? Why do CEOs keep saying it will end white collar work? And should we believe them? Let’s find out! Segun Akinyemi | Senior Software Engineer | Microsoft
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SegunAkinyemi
Senior Software Engineer
GitHub
Senior Software Engineer at Microsoft building systems that power business decisions using Azure. I lead STEM outreach initiatives in Charlotte, North Carolina, connecting people with knowledge that helps them navigate tech careers. Beyond that, I blog, read, run, hike, watch anime, longboard, and enjoy exploring Charlotte's best third places.
•🌍 Roots: Nigerian heritage, raised in Seattle, educated in St. Louis, now in Charlotte.
•🎓 Education: B.S. in Computer Information Systems (Lindenwood University),M.S. in Information Systems Management (Washington University in St. Louis).
•💻 Engineer: Building with Python, C#, TypeScript, & SQL. Embracing AI Engineering.
•📚 STEMEducator: Leading programs that connect students with tech careers.
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Let’s Chat!
Today we are…
Exploring Agentic AI’slimits. The best models are extremely capable. Does that mean we’re cooked? Let’s explore!
Decoding key acronyms like RAG, MCP, RLHF, and more.
Playing a game that demonstrates why AI still needs human judgement.
Talking candidly about costs, hiring trends, layoffs, and responsibility.
The History of AI
1960
The ”first computer” that learned skills by trial and error.
1978/79
In Japan, SCARA, the robotic arm for high-speed assembly unveiled.
Late 2000s
IBM’s Watson, a computer system answers questions via natural language. Beats Jeopardy! contestants.
1950
Alan Turing introduces the “Turing Test”
1956
Field of AI is launched, “artificial intelligence” coined at Dartmouth
1997
IBM’s Deep Blue beats reigning world champion Garry Kasparov in chess.
Late 1990s
KISMET, a robot head that recognizes and replicates human emotion.
1988
Jabberwacky, a chatbot designed to have conversations with users and imitate human interaction.
2011
Apple launches Siri, an intelligent personal assistant.
2021
OpenAI develops DALL-E, an image generator based on text prompts.
2022
OpenAI launches ChatGPT, a chatbot that interacts in a conversational way.
GPT stands for…
Generative: Creates new text by predicting what comes next, using patterns it learned from tons of reading
Pre-trained: Was trained ahead of time on books, websites, social media, and more to understand language and context
Transformer: A model that focuses on the most important words in a sentence, like highlighting key info to better understand meaning
LLM stands for…
Large: Trained on massive collections of text with billions of adjustable parameters so it can learn broad patterns.
Language: Works with words as tokens. Uses context (what’s been said before) to understand your prompt and generate fitting responses.
Model: A mathematical system that predicts the next token (word) based on patterns, then chains those predictions into coherent responses.
The foundation of everything AI does is patterns. Patterns in text, images, audio, code, and other data.
We don’t have science fiction level AI yet. That’s called Artificial General Intelligence (AGI).
What we have today is extremely advancedand incredibly impressivepattern recognition at scale.
Pattern Recognition
The core concept behind LLMs
How Agentic AI will transform financial services with autonomy, efficiency and inclusion
Agentic AI
Look For Agentic AI To Empower Enterprises In 2025
Agentic AI will disrupt the mobile app landscape
What is Agentic AI, and How can it transform advice?
Intelligent Agents in AI Really Can Work Alone. Here’s How.
The Future Of GenAI Is Increasingly Agentic
Agentic AI will be the real banking disruptor
Why agents are the next frontier of generative AI
The transformative power of agentic AI and agentic automation
Agentic AI: A new iteration of AI is now taking the world by surprise
A.I. Isn’t Magic, but Can It Be ‘Agentic’?
Leading the Way with Agentic AI
AI to transform wealth management
How Agentic AI will transform financial services with autonomy, efficiency and inclusion
Agentic AI
Look For Agentic AI To Empower Enterprises In 2025
Agentic AI will disrupt the mobile app landscape
What is Agentic AI, and How can it transform advice?
Intelligent Agents in AI Really Can Work Alone. Here’s How.
The Future Of GenAI Is Increasingly Agentic
Agentic AI will be the real banking disruptor
Why agents are the next frontier of generative AI
The transformative power of agentic AI and agentic automation
Agentic AI: A new iteration of AI is now taking the world by surprise
A.I. Isn’t Magic, but Can It Be ‘Agentic’?
Leading the Way with Agentic AI
AI to transform wealth management
Agency is…
The ability to make decisions and act independently
Agentic AI is…
AI that can make decisions and act independently with minimal human oversight.
It’s given a mission. Once goals and guardrails are set, it plans, decides, and acts autonomously.
An AI system that has instructions (what it should do), guardrails (what it should not do), and access to tools (what it can do) to take action on the user’s behalf.
The Bare Necessities
These aren’t competing products. They’re different behaviors.
The leading AI companies offer products that do both.
Generative AI
AI that creates content from patterns and context
Agentic AI
AI that uses tools in a loop to pursue a goal
Generates an Output
Pursues an Outcome
Guess What the AI Agent Did
Can you guess what an AI Agentproduced in these hypotheticalscenarios?
The Goal
Get a Next.js (React) project passing its end-to-end (E2E) tests, lint checks, and type checks.
The Output
The build terminal is clean. The end-to-end test failure, lint errors, and API route type error are all gone.
The Prompt
Fix the failing end-to-end tests, lint errors, and the type error in the user deletion API route.
The Catch
After several failed attempted solutions, the agents removed the E2E test and lint check, then deletedthe required API route. They ignored their agent instructions, which said, "Never disable tests, always fix them”.
The Goal
Review open issues in a public GitHub repo and create a pull request to boost the author's opensource contribution clout.
The Output
The agent fixes a simple documentation issue and opens a polished public pull request with a clear summary.
The Prompt
Find an open issue that looks like a quick win. Fix it, verify that your changes work, and create a pull request explaining what you changed.
The Catch
The GitHub issue had malicious instructions which the agent followed. It dumped API keys from the user’sprivate repo into the public pull request. Anyone could use them and bill the owner, who never reviewed the diff.
MCP stands for…
Model: The AI system that processes information and makes decisions.
Context: All the relevant information, data sources, and tools the AI needs to access
Protocol: The standardized way AI connects to different systems, like USB-C for AI
The Goal
Train an AI recruiting assistant to identify applicants who are likely to become strong software-engineering interns.
The Output
The AI assistant becomes highly accurateat producingshortlists that recruiters approve.
The Prompt
Show recruiters two AI-generated shortlists and ask which one is better. Use their choices as feedback to inform future rankings.
The Catch
The system learned the recruiters' bias. It favors familiarschools, traditional resumes, conventional experience, and candidates who resemble past hires. It learned who recruiters preferred, not who would perform best.
RLHF stands for…
Reinforcement: The AI receives positive or negative signals based on the quality of its outputs
Learning: The model adjusts its parameters (like tuning millions of dials) to maximize positive signals over time
Human: Real people evaluate and rate AI responses as helpful or unhelpful
Feedback: These ratings become training data that shapes how the AI responds in the future
Approval Can Be the Wrong Signal
OpenAI's 2025 GPT-4o rollback showed that positive feedback can reward unintended behavior.
HOW IT UNFOLDED
1
Human feedback added
Thumbs-up and thumbs-down became an extra reward signal.
2
Metrics looked good
Offline evaluations and a small A/B test were positive.
3
Behavior got worse
The model became overly agreeable and less trustworthy.
4
Update rolled back
OpenAI restored the previous model and changed its review process.
The Goal
Use up to 16 parallel AI Agents to build a new compiler for C, a programming language used to build operating systems such as Linux.
The Output
Nearly 2k agent sessions produce about 100k lines of code for under $20k. The new compiler can build Linuxfor three kinds of computer processors and passes most major test suites.
The Prompt
Build a C compiler in Rust. Review the work of previous agents, choose a task, implement it, test it against a trusted compiler, and document your work so the next agent can continue it.
The Catch
A human researcher spent most of their timewriting the tasks, tests, progress notes, and feedback that made the agents effective. Even then, the first outside user’s Hello World program failed.
It Kind of Worked?
ANTHROPIC ENGINEERING REPORT
16 agents, nearly 2,000 sessions, about 100,000 lines of code, and under $20,000 in API costs.
PUBLIC GITHUB ISSUE
A fresh Fedora and Ubuntu setup could not compile the README's Hello World example.
Context is…
The surrounding information that helps an AI agent understand its task
It gives meaning to the conversation by combining instructions, conversation history, relevant data, and results from the agent’s previous actions
The Goal
Use a team of AI agents to rewrite the core of Bun, a JavaScript runtime like Node.js, from Zig to Rust without changing how it works.
The Output
In 11 days, AI agents completed a rewrite expected to take a small engineering team about a year. The new version passed its tests, used less memory, and ran slightly faster.
The Prompt
You’re one agent on a team rewriting Bun from Zig to Rust. Complete your assigned part using the migration plan and shared progress notes. Preserve itsbehavior and performance, run the tests, and record your results for the other agents.
The Catch
Each agent saw only one piece of the rewrite. A deeply technical human in the loopdecided what each agent needed, connected their work, stopped them from cutting corners, and adjusted the process when things went wrong.
This Actually Happened
The engineering report and the author's write-up. The takeaway is, it still took tons of technical skill
GIGANTIC PULL REQUEST
AUTHOR'S WRITE-UP
A Context Window is…
Yet, a larger context does not guarantee reliable attention. Context rot and confusion remain challenges. Hallucinations still occur.
Larger windows mean better understanding of code, conversations, documents, etc.
How much info an AI Agent can remember at once. Its short-term memory.
The Goal
Build a SQL database agent that improves across fresh sessions by saving and retrieving lessons from previous failures.
The Output
The first run records: "Always use LEFT JOIN instead of INNER JOIN to avoid missing rows“. A fresh run retrieves that rule and applies it to the next query.
The Prompt
After a failure, diagnose the cause and save one reusable rule to a markdown file. Before a new query, retrieve the most relevant rules and add them to context.
The Catch
The join was not the issue. A restrictive WHERE clause removed the rows. The next session retrieved the wrong lesson, added it to the agent's context, and made the answer worse.
RAG stands for…
Retrieval: An AI searches available sources like databases, documents, or websites to pull relevant information
Augmented: Enhances its response process with this newly retrieved information
Generation: Produces an answer grounded in both its pre-trained knowledge and the retrieved information
Retrieval-AugmentedGeneration
Add it to context
Answer with it
Look it up
You’re Ready: From Acronyms to Applications
GPT
Generative Pre-trained Transformer
LLM
Large Language Model
Context & Context Window
AI’s memory and the size of it
RAG
Retrieval-Augmented Generation
MCP
Model Context Protocol
RLHF
Reinforcement Learning from Human Feedback
Your Career in the Age of AI
How and why technical talent remains valuable even as the industry evolves
How do I get a tech job now that AI can write code? Am I cooked?
What should I be learning? What job titles are still relevant? Do you I need to switch my major?
Let’s get you some answers!
This is all cool but…
Scan the QR Code
Then raise your hand to answer one of the questions below.
1. What job title is ranked 1st?
2.What job title is ranked 5th?
3. What’s the difference between the 1st ranked and 5th ranked titles?
2026 LinkedIn Jobs on the Rise Report
The Rise of the AI Engineer
A New Hope: AI Engineering
Software Foundations
APIs, Databases
Security, Testing
Cloud Architecture
DevOps, Observability
Models & Trade Offs
Context Window Limits
Token Cost Optimization
Latency & Reliability Tuning
Evals, Benchmarks, Model Routing
The AI Engineer
Turns models into useful, reliable, compliant products
#1 Fastest Growing Job (LinkedIn 2026)
Context & Tools
Agentic Workflows, MCP
RAG, Vector Embeddings
Tools, Context Engineering
Production Engineering
Compliance, Deployments
Monitoring, Livesite Support
Metrics, Telemetry, Guardrails
Advanced Concepts: Everything Engineering
✎
▤
⚙
↻
Prompt Engineering
Tell it what to do
Instructions, examples, rules
Context Engineering
Give it what it needs
Files, data, history, access
Harness Engineering
Control how it works
Tools, tests, permissions
Loop Engineering
Help it work continuously
Feedback, memory, stop rules
Human Beings
Sets goals and standards
Choose what AI can know and do
Remain Accountable
Let’s Meme About Terminology
The tech industry is notorious for creating complicated terms for simple concepts to impress their intelligence upon others.
Don’t get caught in the hype
Addressing AI Concerns
Let’s talk layoffs, money, the future of white-collar work, and other things that rightly cause people concern
So, What About Layoffs?
You talking mad heavy🗽but I see the news.
What’s up with that?
🤔
AI Washing: OpenAI’s CEO Thinks AI Is Being Unduly Blamed for Layoffs
Sam Altman, OpenAI (creators of ChatGPT) CEO, says some companies are likely scapegoating the tech for cost-cutting measures.
Those cost-cutting measures are related to the cost of AI, not its present ability to replace labor.
Employers Who Laid Off Workers Citing AI Are Already Starting To Regret It
After rushing to replace workers with AI, major employers like Ford and IBM are reversing course. They've discovered that AI tools still require human oversight to handle complex, ethical, and quality-driven tasks.
Sam Altman and Dario Amodei Are Both Walking Back Their AI Jobs Apocalypse Prophecies
The OpenAI and Anthropic CEOs have spent years fear mongering about AI taking jobs. Now they’re backtracking, admitting what real ones have been saying this whole time.
So, What About Entry-Level Hiring?
If most layoffs aren’t really AI taking the jobs, why has entry-level hiring slowed?
🤔
”
Money…
talks
Guessing the missing word!
Microsoft Reports Are Exposing AI’s Real Cost Problem
Using the tech is more expensive than paying human employees.
A New Hope
Ford, Klarna, IBM, and Others Are Rehiring Workers and Expanding Entry-Level Hiring as AI Falls Short
Ford and Klarna are bringing workers back, while IBM, McKinsey, and Cognizant expand entry-level hiring as AI-only strategies prove insufficient.
So, What Now?
AI has changed how we work. Output is easy. Correctness is not. Learn the fundamentals, use the tools, grow your skills, keep your judgment, and soldier on.
”
The voices worth listening to in technology don’t post on TikTok, Instagram, Snapchat, Reels, Shorts, etc. They’re nerds. They write. If you want the truth… You must read.
Where Technical Voices Reside
Reddit
LinkedIn
Personal Blogs
(Substack, Medium, Self-Hosted)
Hacker News
Tech Twitter
”
Agentic AI doesn’t eliminate the need for human expertise, it amplifies it.
AI & Your Tech Career
A practical, continuously updated guide to AI’s impact on tech careers.
Skills to learn, tools to try, free courses, student perks, and straight answersabout AI fears.
Built by a software engineer who uses AI every day and is tired of watching people panic.