Roadmap to Learn Agentic AI
This roadmap breaks down the journey into 12 focused stages:
– Grasp the core differences between traditional AI and autonomous agents
– Build a solid foundation in ML, LLMs, and frameworks like LangGraph, CrewAI, and AutoGen
– Understand how agents use memory, plan actions, and collaborate
– Learn to implement retrieval-augmented generation (RAG) and adaptive reinforcement learning
– Deploy agents in real-world scenarios with performance monitoring and continuous improvement
If you're building AI that goes beyond chat interfaces, this roadmap will help you architect systems that are capable, contextual, and action-oriented.
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90% of engineers using AI coding tools are doing it wrong.
They're treating AI like a code monkey. Fire prompt → Get code → Accept all changes → Ship.
That's why we see 128k-line AI pull requests that became memes (look this up, it's a fun read).
After spending quite a bit of time using AI dev tools, I discovered the real game isn't about generating more code faster.
It's about rapid engineering while managing cognitive load.
My workflow now:
1. Start with AI-generated system diagrams
2. Ask questions until I understand the architecture
3. Create detailed change plans
4. Break down into AI-manageable chunks
5. Maintain context throughout
This isn't coding. It's orchestration.
The best engineers aren't typing anymore. They're conducting symphonies of AI agents, each handling specific complexity while the human maintains the vision.
Think about it → We're moving from IDEs to "Cognitive Load Managers."
Tools that auto-generate documentation, visualize dependencies in real-time, and explain impact before you commit.
The future isn't AI writing code.
It's AI helping you understand what code to write.
The billion-dollar opportunity? Build the tool that turns every engineer into a systems architect who happens to code.
We're not being replaced. We're being promoted.
Who else sees this shift?
#AI#SoftwareEngineering#DevTools#FutureOfCoding#TechLeadership
How do materials fail, and how can we design stronger, tougher, and more resilient ones? Published in #PNAS, our physics-aware AI model integrates advanced reasoning, rational thinking, and strategic planning capabilities models with the ability to write and execute code, perform atomistic simulations to solicit new physics data from “first principles”, and conduct visual analysis of graphed results and molecular mechanisms. By employing a multiagent strategy, these capabilities are combined into an intelligent system designed to solve complex scientific analysis and design tasks, as applied here to alloy design and discovery.
This is significant because our model overcomes the limitations of traditional data-driven approaches by integrating diverse AI capabilities—reasoning, simulations, and multimodal analysis—into a collaborative system, enabling autonomous, adaptive, and efficient solutions to complex, multiobjective materials design problems that were previously slow, expert-dependent, and domain-specific.
Wonderful work by my postdoc Alireza Ghafarollahi!
Background:
The design of new alloys is a multiscale problem that requires a holistic approach that involves retrieving relevant knowledge, applying advanced computational methods, conducting experimental validations, and analyzing the results, a process that is typically slow and reserved for human experts. Machine learning can help accelerate this process, for instance, through the use of deep surrogate models that connect structural and chemical features to material properties, or vice versa. However, existing data-driven models often target specific material objectives, offering limited flexibility to integrate out-of-domain knowledge and cannot adapt to new, unforeseen challenges.
Our model overcomes these limitations by leveraging the distinct capabilities of multiple AI agents that collaborate autonomously within a dynamic environment to solve complex materials design tasks. The proposed physics-aware generative AI platform, AtomAgents, synergizes the intelligence of LLMs and the dynamic collaboration among AI agents with expertise in various domains, incl. knowledge retrieval, multimodal data integration, physics-based simulations, and comprehensive results analysis across modalities.
The concerted effort of the multiagent system allows for addressing complex materials design problems, as demonstrated by examples that include autonomously designing metallic alloys with enhanced properties compared to their pure counterparts. We demonstrate accurate prediction of key characteristics across alloys and highlight the crucial role of solid solution alloying to steer the development of alloys.
Paper: https://lnkd.in/enusweMf
Code: https://lnkd.in/eWv2eKwSMIT Schwarzman College of ComputingMIT Civil and Environmental EngineeringMIT Department of Mechanical Engineering (MechE)MIT Industrial Liaison ProgramMIT School of Engineering
AI didn’t assist engineers here. It designed the rocket engine. What do you think?
LEAP 71 just proved something big for engineering and AI:
• A liquid rocket engine was autonomously designed by a physics-based AI system (Noyron)
• 3D-printed as a single copper part
• Hot-fired successfully on the very first test
• No traditional CAD, no manual iteration loops
This wasn’t trial-and-error.
It was pure physics + computation + manufacturing constraints encoded in software.
Once the model exists, new engine variants can be generated in minutes, not months.
Why this matters:
Rocket engines are among the hardest machines humans build:
• ~3,000°C combustion temperatures
• Cryogenic propellants
• Extreme pressure, vibration, and thermal stress
And yet… the first design worked.
This isn’t “AI will replace engineers.”
This is engineering moving from drawing to defining intent — and letting computation do the rest.
Same shift we’re seeing in:
• Semiconductors
• AI infrastructure
• Advanced manufacturing
• Robotics & simulation
Design is becoming software.
Testing is becoming data.
Iteration speed is becoming the real advantage.
The future of engineering just fired on a test stand 🚀
#AI via @codeintellectus and Joel Gomes #Engineering#Aerospace#ComputationalDesign#AdvancedManufacturing#3DPrinting#DeepTech#Innovation
AI is changing what problem-solving means in tech.
Earlier, problem-solving often meant figuring out how to build something.
Choosing the right algorithm.
Optimizing performance.
Writing clean code.
Today, the “how” is no longer the hardest part.
AI can generate code, suggest architectures, and fix syntax in seconds.
What has become difficult is deciding what to build and why.
Real problem-solving now starts much earlier.
🔸Understanding vague requirements.
🔸 Translating business needs into technical decisions.
🔸Choosing trade-offs that will age well.
🔸Knowing when a solution is good enough and when it is over-engineered.
AI accelerates execution.
It does not replace judgment.
Strong engineers today are the ones who can
👉ask the right questions
👉narrow down the real problem
👉make decisions with incomplete information
👉and take responsibility for those decisions
AI changed the surface of problem-solving.
Not its core.
The core is still thinking clearly in messy situations.
And that skill is becoming more valuable, not less.
Turning AI into enterprise value | $XX M in Business Impact | Speaker - MHA/IITs/NITs | Google AI Expert (Top 300 globally) | 50 Million+ views | MS in ML - UoA
Most AI portfolios look the same.
RAG chatbot. Sentiment analysis. Maybe a fine-tuned model.
Recruiters have seen it 500 times this week.
You need to show off "Multi-Agent Systems" to get you noticed in 2026.
The global agentic AI market is projected to grow from $5.1B in 2024 to over $47B by 2030. Every major tech company, from Google to Microsoft, is racing to hire engineers who can build systems where multiple AI agents coordinate, communicate, and act autonomously.
The problem? Most people don't know where to start.
So here are 10 project ideas that show you can build what the industry actually needs:
🥦 Smart Traffic Control System
Fixed signal timings cost urban economies billions annually. Build agents that dynamically adjust signals using real-time traffic data.
🥦 Disaster Response System
Poor coordination in emergencies costs lives. Build agents for rescue teams, drones, and medical units that allocate tasks dynamically.
🥦 Autonomous Warehouse System
Amazon alone operates 750,000+ robots. Build a system where inventory agents and robot agents collaborate on storage and delivery.
🥦 Multi-Agent Stock Trading Simulator
Algorithmic trading accounts for 60-73% of US equity volume. Build competing trading agents, a market maker, trend follower, and arbitrage agent, in a simulated environment.
🥦 Smart Energy Grid System
Up to 8% of electricity generated globally is lost due to distribution inefficiency. Build agents that balance demand across homes, grids, and solar sources.
🥦 Delivery Drone System
Last-mile delivery accounts for 53% of total shipping costs. Build drone agents that coordinate routes and avoid collisions.
🥦 Medical Diagnosis System
Diagnostic errors affect approximately 12 million Americans annually. Build specialist agents (cardiology, radiology, pathology) that collaborate on patient data.
🥦 Multi-Agent Game AI System
The global gaming market is worth $200B+. Build RL-based agents that compete and cooperate in a simulated game environment.
🥦 Environmental Monitoring System
India has 14 of the world's 20 most polluted cities. Build distributed sensor agents that detect anomalies in air and water quality in real time.
🥦 Personal Assistant System
Build a planner agent, research agent, and executor agent that collaborate to handle complex, multi-step tasks, the architecture behind every serious AI product being built today.
Each of these maps to a real-world problem, a real industry, and a real hiring need.
You don't need to build all 10.
You need to build one, document it well, and explain the architecture clearly. That alone puts you ahead of 90% of applicants.
Which one are you building?
If you’re an aspiring AI engineer trying to understand how the industry is moving beyond LLMs, here’s a quick eagle’s-eye view of one of the most fascinating frontiers in AI today: 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗦𝘆𝘀𝘁𝗲𝗺𝘀.
We’ve reached a point where large language models can generate text, summarize papers, write code, and even reason, but that’s not enough anymore.
The next leap isn’t about bigger models. It’s about autonomy, with systems that can not only generate but also decide, act, and adapt in the real world.
That’s where Agentic AI Systems come in.
These are goal-driven, adaptive platforms capable of orchestrating complex workflows, making independent decisions, and using memory to 𝗥𝗲𝗮𝘀𝗼𝗻 → 𝗔𝗰𝘁 → 𝗔𝗱𝗮𝗽𝘁.
Instead of just prompting a model for a single response, you’re designing a network of intelligent components that:
→ Understand goals and constraints
→ Plan actions through orchestration frameworks
→ Execute via tools, APIs, or other agents
→ Observe results, learn, and improve over time
This shift, from intelligence to autonomous intelligence, is why agentic systems have become one of the most important topics for modern AI engineers.
𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀
→ For AI Engineers:
Agentic architectures are redefining how applications are built- from RAG pipelines and copilots to autonomous research or data systems. Understanding gateways, planners, orchestrators, memory layers, and evaluation loops will become a must-have skill set.
→ For Tech Leaders:
If you’re leading teams or evaluating where AI fits into your business, this is your blueprint for understanding how next-gen systems will operate- safely, scalably, and with clear policy and observability layers.
Happy learning & Happy Building 🚀
The Complete Guide to Building with Google AI Studio
Google launched an AI prototyping tool and I decided to provide a hands-on, step-by-step guide to prototyping with it —from your first chatbot to production-ready multimodal apps that combine text, images, video, voice, and real-time data. Whether you’re prototyping ideas, a founder testing concepts, or a developer exploring rapid engineering, this guide will show you how to leverage Google’s AI stack without writing a single line of code (unless you want to).
I think this works well when you:
- You want to prototype multimodal apps (text + image + video + voice)
- Need location-aware or grounded apps (Maps/Search)
- You’re experimenting on a budget
- You’re a PM/non-technical founder testing ideas. Lowest learning curve with highest ceiling. You can export to code when ready.
- You want one-click deployment for demos
Cloud Run deployment is frictionless. Competitors require more setup.
- You need 1M token context for complex projects
📌 my guide: https://lnkd.in/gx6wFT4b