The Agentic Revolution: How AI Agents Will Reshape Software, Startups, and Work
From personal copilots to enterprise automation, here’s what every founder and investor should know about the rise of AI agents—and why this shift may be as big as the cloud and mobile revolutions.
This isn’t just another tech wave.
If the cloud gave us SaaS, and mobile brought us the App Economy, then AI agents might be the next big shift — a new layer of software that doesn’t just help us work, but works for us.
Not dashboards. Not copilots. Real autonomy.
Call it the Agentic Revolution.
And like every shift before it, this one won’t roll out evenly. Some founders will ride it early. Others will miss it entirely.
Over the past few months, I’ve been going deep into this space — talking to builders, investors, and founders working at the edge of what agents can do. In this piece, I’ll break down:
Why AI agents might be as big as the cloud or mobile
How they’re already reshaping software, business models, and work
What this means for anyone building or investing in tech right now
If you’ve been trying to make sense of all the agent hype — or wondering where the real opportunities are — this guide is for you.
👋 Hello! I’m Ivan. I write a newsletter for early-stage founders and invest in startups at JME.vc. Previously, I built products at Facebook and was part of Bloomreach as it scaled toward $2B+. I also love surfing and BJJ.
🌊 Agentic Revolution
“We’re at the beginning of a new Industrial Revolution. But instead of generating electricity, we’re generating intelligence… [Open source] activated every single company. Made it possible for every company to be an AI company.”
— Jensen Huang
We might be approaching a “ChatGPT moment” for AI agents.
It seems like humans might have figured out how to scale intelligence.
There’s a lot of noise out there.
So here’s are a few key insights and frameworks from the best resources I’ve found to help you wrap your head around this emerging tech wave:
1. AI Agents are probably simultaneously over and under-hyped
"I think the AI boom will rhyme with the dot-com bubble."
I think we are in a bubble, but bubbles have different shapes. I think the AI bubble will rhyme with the dot.com bubble. Most of the excess of the dot.com bubble might have been justified. If you look at the top market cap companies in the world, they include Amazon, Google, Paypal, eBay, Salesforce. All of these were started in the dot.com bubble. There are areas of excess today, but it would be dangerous to dismiss this as strictly excess, and there’ll probably be outsized returns within it.
- Brett Taylor
Here’s a snapshot of AI-first (?) companies revenue Vs valuation, you get the picture:
Combined valuation of >$17B
Combined revenue <$100M
2. The rise of the “Agentic Economy” is a potentially (very) big deal
Every major platform generational shift has prompted the rise of a new type of economy. Every time, they have given rise to huge, established tech players.
The same players that you have probably been reading about lately, about how they are scrambling to leapfrog to the next rising economy.
Here are a few examples you’ve all experienced first-hand:
Public Cloud enabled the SaaS economy
The iPhone enabled the App economy
Social media enabled the Creator economy
LLMs gives rise to the Agentic economy
And here’s why it is potentially very exciting:
AI agents are changing traditional software – Instead of the usual click-around menus, separate data, and pay-per-user model, AI agents do things differently.
AI agents reduce the need for human labor – Companies spend a lot more on workers than on software (35x+). AI agents can help cover both areas, potentially saving on labor costs.
AI agents make services more efficient – In industries where work is done by people and profit margins are low, AI agents can boost productivity and reduce costs.
3. There are 3 types of emerging AI Agents: Personal, Role-Based, and Company-Focused
“Autonomous agents are programs, powered by AI, that when given an objective are able to create tasks for themselves, complete tasks, create new tasks, reprioritize their task list, complete the new top task, and loop until their objective is reached.”
—Matt Schlicht
Agents have the potential to be so useful that you might see them everywhere soon:
4. The way AI Agents are built challenges traditional software development practices
A new kind of software demands a new approach to development.
- Clay Bavor
As AI agents replace traditional rule-based software, they challenge established development practices by introducing unpredictability, high costs, and unique upgrade issues.
Adapting to this shift requires new strategies for reliability and future-proofing.
Digital Shift: Software development has evolved with structured best practices (SDLC) for reliability.
AI Agents Challenge SDLC: Unlike traditional software, AI agents use flexible, goal-based models, creating unpredictable results.
Input Differences: Structured input (forms) vs. natural language, leading to infinite interaction possibilities in agents.
Performance & Cost: Traditional software is fast and low-cost; agents rely on slower, more expensive LLMs.
Upgrade Instability: Traditional updates are smooth; LLM updates can disrupt agents, requiring re-training.
New Paradigm: Moving from deterministic, affordable software to adaptive, costly AI agents poses new reliability challenges.
5. Commercially, the AI market might play out like the cloud market
What learnings could we derive from how the cloud market played out, that might “rhyme” with what is currently going on in AI?
Well, 3 big categories emerged:
Infrastructure as a Service: Azure, Google cloud, AWS…
Tool makers: Datadog, Snowflake….
SaaS: Salesforce, Adobe, extremely long-tail of solutions.
The SaaS category has the most 2B-20B company outcomes, as represented in the stock market - and we might see something similar happen with this wave.
Much of the current media and VC focus is on data centers and foundation models.
The current “consensus” is that this is the safest layer to be in. Group-think dictates that you know that whatever happens on top, those layers will “collect taxes”. But…
The idea that somehow the way the world wants to buy software will change becuase these foundation models are really smart doesn’t resonate.
—Brett Taylor
And if this wave does end up rhyming with the cloud market…
6. Applications might be the most exciting area
Labor budgets are 35x software budgets, and AI agents eat into both.
A few takes on short-term impact:
𝗛𝘂𝗺𝗮𝗻 + 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗳𝗶𝗿𝘀𝘁: In the short term, a "human + machine" angle still wins. Industries with built-in failover mechanisms (like customer support) are ideal for initial AI deployment, allowing AI to handle basic tasks now and more complex ones over time.
𝗥𝗲𝗶𝗻𝘃𝗲𝗻𝘁𝗶𝗻𝗴 𝗣𝗿𝗶𝗰𝗶𝗻𝗴: AI agents challenge traditional SaaS models by moving beyond point-and-click interfaces, siloed data, and seat-based pricing.
𝗘𝘅𝗽𝗮𝗻𝗱𝗶𝗻𝗴 𝗔𝗰𝗿𝗼𝘀𝘀 𝗠𝗮𝗿𝗸𝗲𝘁𝘀: AI agents are transforming industries by deploying across horizontal applications (cross-industry tools like legal AI for enterprises), vertical applications (industry-specific solutions like AI for law firms), and consumer services (full-stack services like legal assistance apps).
𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗼𝗻 𝗺𝗼𝗮𝘁: Rapid advancements in foundational AI models may commoditise a lot of early / short-term winners' use-cases. Raises questions on what sectors and verticals are more suitable to build product moats around. AI’s accuracy in regulated industries is improving (these plays could have a significant moat).
7. An Agent Infrastructure layer is needed for the ecosystem to blossom
There are a few pre-requisites for this to take place:
Privacy is key – Since agents handle a lot of personal data, they need strong privacy protections.
Efficiency and speed matter – Running agents on devices directly (instead of remotely) keeps costs down, response times fast, and memory usage low.
Agents need to work together – Agents should be able to communicate and work with each other smoothly, even across different platforms (like different calendar agents).
Memory for improvement – Agents should remember past actions to improve over time and become more personalized, even anticipating needs.
Accuracy safeguards – Built-in checks prevent agents from providing wrong or misleading information, helping maintain quality and trustworthiness.
Examples of scaffolding needed to build agents around large foundation models:
Auth: Anon secures agent identity, enabling potential automated actions like flight booking.
Security: Invariant Labs builds AI security for trusted, adaptive protection.
Frameworks: Langchain, LlamaIndex lead in agent app dev, with major F500 adoption.
RAG: LlamaIndex, Unstructured add real-time data for smarter agent responses.
Orchestration: Multi-agent systems delegate tasks for complex queries.
Runtimes: Modal, Browserbase offer scalable, low-latency environments.
Routing: Martian, DSPy optimize model costs and prompt efficiency.
Memory: MemGPT lets agents remember, personalizing interactions.
Evals: Weave (Weights & Biases) tests/improves agent performance.
No-Code: Brevian enables code-free agent deployment for businesses.
8. Agent adoption might come in 3 waves
Wave 1: Text-Based Agents – Early adoption in text-driven roles (e.g., marketing, paralegal support, and customer service) where single-modal, language-based tasks are handled efficiently.
Wave 2: Multi-Modal Agents – Expansion into complex, multi-sensory fields (e.g., architecture, gaming, and education) requiring more diverse skills and inputs.
Wave 3: Regulated Industry Agents – Slower adoption in regulated sectors (e.g., healthcare and finance), where privacy is critical; success often hinges on building trust through advisory boards and compliance credibility.
9. We will see new Agent-development jobs
The web created jobs like UX designers and PMs.
AI agents will do the same.
AI doesn’t replace people, AI + human replaces people, raising the quality bar.
A few examples:
Agent Engineer: Building, deploying, and optimizing agents.
Agent Architect: Crafting effective and engaging agent conversations.
AI Workflow Designer: Designing workflows that use agents to automate tasks.
Ethics Officer: Ensuring agents behave ethically and comply with regulations.
Agent Integration Developer: Connecting agents to various tools to make them more useful.
10. How do you prepare yourself for this wave?
“Every abundance creates a new scarcity. The task for builders is to figure out which resources AI makes abundant, which are rendered scarce, and where an edge can be formed. So far, for those who have found an edge, the business results are incredible.
Think about what resources does AI Agents make scarce:
Focused Attention: funny, considering Google’s paper that kicked this of.
Original Art & Writing: unique, unmistakably human creativity.
Trusted News & Facts: verifiable, AI-proof information sources.
Data Protection Skills: data privacy and security for AI-sensitive environments.
Customer Empathy: in-depth, nuanced, relationship-building skills.
Green Computing: energy-efficient, sustainable computing solutions.
AI Ethics and Governance: frameworks for transparent and ethical AI uses.
Complex Problem Solving: abstract reasoning and strategy beyond AI’s capabilities (we’ll see).
📈 Recent investments
At JME.vc, we're interested in this space and have already made several investments:
Hoola is developing a powerful AI agent shopping assistant for e-commerce.
Rauda is building a customer service agent capable of not only answering questions but fully resolving issues.
Genesy creates sales agents + data sourcing that can autonomously handle and enhance prospecting.
GPTAdvisor helps private bankers serve their clients more effectively and efficiently - at the intersection of agents and copilots.
A few more to be announced.
If you are building in this space, I’d love to learn about it - get in touch!
🌊 AI Agents: 15 Must-Reads
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🌊 Pitch Magic AI: aka learning-by-doing
To my surprise, the Startup Pitch Deck GPT that I built on OpenAI now has over 25K+ conversations, >900 reviews and ranks n.1 for "startups” in the GPT Store.
I wanted to experiment with this little toy and see if I should build a wrapper around it.
I’ve written about how to build a basic GPT and jailbreak it from OpenAI’s wallgarden via their API before - and wanted to take it one step further.
But first, I want to pre-validate wether its worth building or not.
There are two possibilities:
Having the free GPT on the OpenAI store provides enough value, and might be monetizable when they decide to do rev-share or open a paywall feature.
People might be willing to pay for a fine-tuned, more advanced, pitch deck feedback co-pilot.
So, I started diving into emerging GenAI programming tooling.
And let me tell you, it is pretty mind-blowing how far you can get using ONLY natural language. Of course, you still need to fight bugs, and put a lot of hours into it - but I’m seeing tools every day that lower these barriers to entry at a phenomenal speed.
Here’s what I’ve gotten to:
Step 1: Write a PRD
Step 2: Whiteboard a landing page
Step 3: Develop a first landing page with natural language on v0.dev (above)
Step 4: Develop a plan with Claude.ai on what your architecture, user flows, and development stages will look like.
Step 5: Open Replit, start implementing your plan with the code provided by Claude (or any other tool you feel comfortable with). Here’s where the rubber meets the road and you’ll have bugs and have to drive back, keep track of your conversations (because Claude has memory / conversation limits), and fight the good fight. I’ve gotten past building basic sign-up / login functionality, setting up Firebase and experimenting with forking a Github repo for the basic app-experience (very similar to ChatGPT + a library of deck assets potentially).
P.s. also found Bolt.new, which is sort of where this whole space is going towards (all-in-one solution for planning + building + deploying web app).
In my opinion, with steps 1-3, you should have enough to go out there and pre-validate whether something is worth building, or not (saving you countless headaches).
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Ruben x Ivan, such a cool collab