AI Agents That Can Login to Your Systems: The Authentication Revolution

A deep dive with Shubhakar, founder of Moon Devs and Micro Fox, exploring how AI agents are breaking free from chat limitations through authentication systems, multi-agent workflows, and the emergence of fractionalized economy

July 23, 2025

12 min read

By Rachit Magon

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We're living through a quiet revolution in AI development. While most "AI tools" are just chatbots with fancy interfaces, something fundamentally different is emerging: AI agents that can actually log into your systems and perform tasks just like humans do.

The problem? Authentication has been the missing piece of the puzzle. Today's guest has spent over a decade in these trenches, helping more than 30 startups navigate the complex intersection of AI, authentication, and real-world production systems.

Meet Shubhakar, founder of Moon Devs and Micro Fox, where he's building something that sounds like science fiction but is happening right now: AI agents that write code to write code, creating endless loops of autonomous software creation.

Key Takeaways: The Future of AI Agents and Authentication

The Authentication Problem:

• 90% of current "AI tools" are just chatbots with integrations
• Real AI agents need authentication to perform actual tasks
• MCP OAuth and A2A protocols are competing to solve this challenge

Multi-Agent Systems Reality:

• Single super-smart agents are token-expensive and inefficient
• Multi-agent systems allow division of labor and cost optimization
• The future: One orchestrator managing specialized sub-agents

Economic Transformation:

• Fractionalized economy emerging where individuals can build entire companies
• Pay-as-you-go model replacing expensive monthly subscriptions
• Jobs shifting from execution to management and creative problem-solving

Q: Can you give us a background about yourself and introduce Micro Fox?

Shubhakar: I'm Shubhakar, been in the industry for about 10 years now. Started as a freelancer, then got into contracts, and eventually started my own company, Moon Devs, working with startups as a tech advisor. Now we're working on something really gargantuan called Micro Fox.

It all started when a client asked me to build a Discord and Slack bot. While building that, I noticed the code wasn't complicated, and AI coding capabilities were getting really smart. I thought: why not make code that writes code to build these bots?

But then I realized there's a much bigger playground here. We're not just talking about one-layer solutions like Bolt or Lovable where you automatically generate code. We're building three layers deep.

Layer 1: Basic code generation
Layer 2: Code that writes code - coding agents that can build, fix, upgrade, and maintain entire websites
Layer 3: Code loops - code that writes code that writes code, forming circular autonomous systems

🔥 ChaiNet's Hot Take: This isn't just automation - it's self-evolving software. Shubhakar's team has created agents that trigger other agents in endless cycles, with only a 21.54% failure rate.

Q: What's the fundamental difference between chatbots and AI agents?

Shubhakar: The differentiating factor is quite simple: tools.

In a chatbot, you have an LLM model that's capable of texting with you. In an AI agent, you have tools. Each tool can have sub-agents, orchestrators, sequential processes, or simple API calls. These tools give the LLM model the capability to actually do stuff.

This became possible primarily in October when tool calling abilities emerged. That's when MCP shot off and AI agents started blooming everywhere - LangGraph, LangChain, all the mass adoption happened because of that one crucial point: tool calling.

🔥 ChaiNet's Hot Take: Tools are the weapons that transform passive chatbots into active agents. Without tools, you're just having expensive conversations.

Q: Why build multi-agent systems instead of one really smart agent?

Shubhakar: It all comes back to tools and token cost optimization. Take our BuildFox agent - it's not a single agent inside. There are multiple sub-agents: one does research well, one does code writing well, one does planning well.

When you divide work into multiple agents, you get tighter control as a developer and can fine-tune at each level for token optimization. If you want to scale and publish a product, you have to focus on token cost. One bloated agent with all tool calls becomes way too expensive.

Think of it like corporate structure - you have your CEO (the orchestrator), then managers (specialized agents), then teams (specific tools). The competition isn't multi-agent vs. one smart agent - multi-agent systems support that one big smart orchestrator.

🔥 ChaiNet's Hot Take: Economics drives architecture. With GPT-4 costing $30 per million tokens for input, efficient agent design isn't just good practice - it's business survival.

Q: What's the difference between AI agents and AI assistants?

Shubhakar: Great question. The AI assistant is where the reasoning competition happens - Gemini 2.5 vs Claude vs Llama, all racing to become that model that can trigger any AI agent effectively.

But AI assistants have a different superpower: they know the user far better. The assistant's power doesn't come from reasoning capabilities, it comes from knowledge capability of understanding the user. It can give contextual guidance to LLM models to get jobs done perfectly for that specific user's perspective.

So you have two different competitions happening: one is reasoning competition, one is UX competition where companies compete to seduce users into using their assistant.

🔥 ChaiNet's Hot Take: We're seeing the emergence of two AI races: the backend reasoning war and the frontend relationship war. Google Assistant, Siri, and new players are fighting for your daily digital relationship.

Q: Does this leave space for smaller startups against big corporations?

Shubhakar: Actually, Micro Fox is prepping to become an AI assistant too. If a four-man team like us can build thousands of packages in seven days, what overhead do big corporations really have over us?

The distance has decreased significantly. There's still distance - no comparing with Google - but comparatively, it's giving startups and founders superpowers to try new things.

Big companies have restrictions: they're public companies with legal bindings, privacy policies, country regulations. They have their own restrictions tying up their innovation. Startups should see that as an opportunity.

🔥 ChaiNet's Hot Take: AI is the great equalizer. While big tech has resources, they also have bureaucracy. Startups have speed and focus - often the winning combination in emerging markets.

Q: What is a fractionalized economy and how does AI create it?

Shubhakar: The fractionalized economy means you wake up, see a list of jobs you can do that day, pick one, do it, get paid. You're not working for any big company or small company - you pick your own job, finish it, done.

This happens when individuals get bigger capabilities. Right now, I need to work for a company because they handle marketing, HR, sales - I can't do all that myself. Fractionalized economy comes when one person can build a company by themselves using marketing agents, sales AI, everything.

When that power arrives, that's when you'll see real job transformation and the fractionalized economy taking place.

🔥 ChaiNet's Hot Take: We're moving from "employment" to "engagement." The gig economy was just the beginning - the agent economy will let individuals operate at enterprise scale.

Q: What should small developers build if they can't compete with OpenAI?

Shubhakar: No single developer can compete with OpenAI - it's too big. But you have two paths:

Path 1: Research department - understanding ML, transformers, the deep science
Path 2: Application layer - even if researchers build fantastic scientific stuff, it still needs to be applied in the real world

You can start playing with AI SDK, LangGraph, whatever you're good at. When you first build an AI chatbot, you won't know the depth. Build an AI agent, you'll see more capability. Go deeper, and you'll be sitting here doing next-level stuff like me.

Don't get afraid of it. Get started. It's not that difficult - it's easy to grasp as long as you put in effort.

🔥 ChaiNet's Hot Take: The application layer is where individual developers can shine. While OpenAI builds the engine, you build the car that people actually want to drive.

Q: Why do we need tools like AI SDK, and what makes it special?

Shubhakar: AI SDK is your full-stack system for contacting any LLM model - Google Gemini, Llama, anything. They have adapters and tool calling setup already built.

What's special about AI SDK versus LangChain is that it's built by the same team that built Next.js - the most popular platform for building websites. So they provide incredibly good Next.js integration and streaming support.

Streaming means messages start appearing as they come in, not all at once. AI SDK v5 introduced prepare steps where you can control which tools to call mid-process - powerful features for building multi-agent systems with really good user experience.

🔥 ChaiNet's Hot Take: Developer experience matters. AI SDK's streaming capabilities and Next.js integration show how technical decisions impact user satisfaction in AI applications.

Q: What is MCP OAuth and why is it potentially game-changing?

Shubhakar: OAuth has been around 5-10 years as a standard for API security. Instead of API tokens giving generalized access, OAuth gives user-level access. Reddit can give me OAuth, and I can perform Reddit actions for that specific user.

That's exactly what AI assistants need - to perform actions in your place. But MCP was designed for developers running servers locally, not for OAuth integration. OAuth needs to be at the server level for security, not client level.

MCP OAuth was created about 30 days ago, still in beta, because MCP was designed for client-side use, not backend. They're limited by that original architecture.

🔥 ChaiNet's Hot Take: Authentication is the final frontier for AI agents. Once solved, agents won't just read your data - they'll act on your behalf across every system you use.

Q: What about Google's A2A (Agent-to-Agent) protocol?

Shubhakar: A2A comes from Google wanting their host agent to interact with any AI agents out there. It requires a standard interface and agent discovery system.

The biggest problem with A2A: not many adopters because of cost issues. In MCP, the user bears the cost - their API key, their server. In A2A, whoever built the agent bears the burden. That requires proper distribution and payment systems we're not ready for yet.

We support A2A protocol in Micro Fox, but it's not much use because there's no host agent, and I can't give my agents for free - they cost computational usage and tokens. Until pricing is figured out, A2A won't reach its peaks.

🔥 ChaiNet's Hot Take: The agent economy needs its payment rails. Without solved micropayments and usage-based billing, agent-to-agent commerce can't scale beyond proof-of-concepts.

Q: What becomes possible when agents can authenticate and communicate with each other?

Shubhakar: Right now, AI agent powers are monopolized by SaaS companies. There's one specific tool good at voice, another like my Package Fox good at building packages. They've done lots of work to make their agent good in specific domains, but they're monopolizing it.

You pay $30 for one tool yearly, $20 for another, $50 for another - but you never use the entire $50 worth.

When payment systems come into place, all these AI tools get used by one central AI assistant, paying what it's worth for you. Pay-as-you-go credit mechanism instead of paying $30 for each AI tool. And there will be endless AI tools coming.

🔥 ChaiNet's Hot Take: We're moving from subscription fatigue to usage optimization. Instead of 15 different $20/month tools, you'll have one AI assistant that calls specialized agents and pays per use.

Q: What skill will become more valuable when AI can log into systems and do tasks like humans?

Shubhakar: Times will keep changing, so there's no permanently fixed skill except management. But if I had to hire developers now, you need to be good at one of two things:

First: Be incredibly good at reasoning and grasping abilities. Understand any level of complexity by reading code that coding agents print out. Powerful debugging capabilities - see code, know where the error is. That comes from experience building lots of stuff.

Second: User experience. Designers aren't going to do UX anymore - those times are long gone. Front-end developers do design themselves now. They know where to place buttons, colors, fonts. Most front-end developers are already good at this.

AI will never become good at design because human perception changes. What's trendy today won't be tomorrow. Apple said "no red button on top right, I'll put it top left." Design is fundamentally different from what came before.

You also have to stop treating AI coding assistants as assistants. Now coding is about describing objectives to Cursor like talking to a junior developer. That capability comes from management - you need management skills to talk with AI agents better.

🔥 ChaiNet's Hot Take: The future developer is part debugger, part designer, part manager. Technical skills remain crucial, but communication with AI systems becomes equally important.

Final Thoughts: The Road Ahead

Shubhakar's workshop insight: "If you're a developer, I have a workshop starting this Friday about building a tracking coding agent running in GitHub workflows that automatically writes scripts in your repos. It'll be a good breakthrough experience if you've never built a coding agent."

The bottom line: We're not there yet, but we're not very far away either. The infrastructure for AI agents that can authenticate, communicate, and act autonomously is being built right now. Whether you're building agents, evaluating strategies, or trying to understand how this reshapes your industry, the key is getting started.

Don't get scared. Start playing with it. The future belongs to those who understand both the technical possibilities and the human elements that will always matter.

Q: How can people connect with you and continue learning?

Shubhakar: I'm very active on LinkedIn - that's the best place to reach me. You can see what I'm doing professionally and what I'm currently pursuing. If you have questions, I'm open to providing guidance and can chat anytime.

For developers interested in hands-on experience, join my workshop on building coding agents that run in GitHub workflows. It's designed to give you that breakthrough experience with agent development.

Final words: The authentication revolution isn't coming - it's here. AI agents are breaking free from chat limitations and entering the real world of systems, authentication, and autonomous action. The question isn't whether this will happen, but how quickly you'll adapt to lead in this new landscape.