Six months ago, AI shifted. Not in subtle ways. The world went from "let me use a model locally" to "let me deploy agents everywhere and step back." I did that too: went from running models on my laptop to agents talking to me across Telegram, Claude Code, and cloud deployments I rarely touch.
Looking back, the playing field has completely changed. Not just the tooling. The entire path from idea to working system has collapsed, and the people building things are learning to think in new ways.
This is what six months taught me.
The Problem: "Vibe Coding" Was Real, Now It's Mandatory
Six months ago, people were skeptical. "Vibe coding" (letting an AI generate code without explicit structure) felt risky, unserious, a phase.
That phase is over. It became the default.
What's wild is the shift in who's doing it. Six months ago, non-developers talked about AI as a tool. Now developers openly code with agents, and the conversation isn't about whether to use an agent; it's about which one, and how fast you can iterate with it.
The developers who stand out aren't the ones who write the most code. They're the ones who know how to direct an agent, review its output, catch when it's being verbose, and ship the work. Your resume is no longer "I can code in Python." It's "I built this system, here's why it's shaped this way, and here's how it solved a real problem."
Degrees don't matter anymore. Certificates don't matter. Your work is your resume. What you've built, how you think about trade-offs, and what problems you've solved: that's what employers are looking for now.
The Token Economy Is Confusing, and That's the Point
New terms float around like they've always been here: "token maxxing," "tokenomics," "credit usage." People argue about per-token pricing, pro vs. max plans, which provider gives the best value for their use case.
Here's the thing: nobody fully understands the economics yet. We're learning this in real-time, and the guardrails are still being built.
Consider Anthropic's release of Claude models: a few days later, features get blocked due to security concerns. Developers put API keys in plain text because they don't know better. The standards and policies we're supposed to follow aren't stabilized yet. It's like the early internet: useful, chaotic, and generating problems we'll spend the next few years fixing.
That's not a criticism. It's the nature of new tech. But it means you can't "wait for best practices"; you're living through their creation.
What I've Actually Learned (In No Particular Order)
1. No One Predicts Six Months Out
I can't tell you what the agent landscape looks like in six months because I have no visibility into policy, model releases, or which providers survive the consolidation. That's fine. Stop trying. Focus on what works now, not what might work later.
2. Know Your Models and What They Actually Do
There's a massive gap between "Claude is good at reasoning" and knowing when Claude is worth the cost. Same with every model. Learn their actual strengths, failure modes, and when they're overkill. Don't treat all LLMs as interchangeable.
3. Don't Lock Yourself Into One Provider
This is critical. If your entire workflow depends on Claude, and Anthropic changes pricing or releases a breaking change, you're stuck. Use OpenRouter instead; it abstracts the provider layer. You pay for what you use, you get access to dozens of models, and you can swap providers without rewriting your agent pipeline.
If you're in an enterprise environment or building for enterprises, knowing Anthropic's Claude CLI and the Claude API is worth it. For everyone else, router-based abstraction buys you optionality.
4. Agent Orchestration and Automation Are the Emerging Layer
Agents doing one task are useful. Agents coordinating other agents? That's where the work is moving. Tools like agent frameworks (which handle multi-step workflows, memory, and tool calling) are becoming infrastructure. The agents you write a year from now won't be single-turn scripts, they'll be orchestrators managing other agents.
5. Understand Tokens and Context Depth
This sounds obvious, but it's not. Most people don't think deeply about how much context their agent has, what happens when context fills up, or how to structure queries to minimize token waste. Understanding tokens and context windows can completely reshape how you design agents, especially when running many of them in parallel or on schedules.
Start with this deep dive on tokens and tokenization, it covers the fundamentals clearly. Once you understand what a token actually is, how models consume them, and why context depth matters, everything else clicks into place.
For enterprise infrastructure, this matters even more: Copilot, Power Automate, and other business platforms all have hard token limits and context constraints. Design around them or fail silently.
6. You're No Longer Bound to Pre-Built Services
This is the mind-shifter. You want a VPN service? An agent can build one. A finance app? A game? An AR app? Home assistant integrations? Robotics?
These aren't decades out. Agents can be trained on your specs, iterate toward working solutions, and deploy them. The constraint used to be "I need to hire a developer." Now it's "I need to know how to direct an agent toward a solution."
Robotics and home automation are especially going to blow up in the next decade: agents are the missing piece that makes them practical.
What's Next
I'm going to dig deeper into these themes in upcoming posts:
- How to design agent workflows that don't explode token costs
- Comparing orchestration tools and when to use which one
- Real examples of agents that coordinate other agents
- Token economics across different providers
- Why context depth matters more than model size
The shift from "I built a tool" to "I directed an agent to build it" is permanent. The winners will be the people who understand both the tools and the thinking.
Resources
Model Access & Routing:
- OpenRouter - Model router with abstraction over multiple providers
- Anthropic / Claude API - Direct access to Claude models
- Claude Code - IDE integration and CLI for agent-driven development
Agent Communication & Platforms:
- Telegram Bots - Accessible agent interface for remote communication
- Anthropic Documentation - API reference and agent patterns
Understanding Tokens & Context:
- Tokens and Tokenization Explained - Foundational understanding of how models consume tokens
Learning More:
- Keep tabs on token pricing and provider announcements; they move faster than blog posts
- Experiment with multi-agent workflows on your own stack
- Build with agents that can swap models, not ones hardcoded to a single provider
The most important lesson: Don't wait for stability. The people who understand agents best right now are the ones learning by building, not the ones reading think pieces about where it's all going.
Build something. Direct an agent. See what breaks. Adjust. Ship it.