Six Months in AI Feels Like Six Years
If you blinked, you missed it. The gap between Q4 2025 and Q1 2026 wasn't just another quarter—it was a generational leap in how we build with AI.
The Shift Nobody Talks About
Six months ago, we were still debating whether AI agents were practical. Now the question isn't if you use agents—it's how many and what for. The conversation shifted from "Can agents be useful?" to "How do I orchestrate ten of them without losing my mind?"
The MCP (Model Context Protocol) ecosystem exploded. What started as Anthropic's experimental protocol became the de facto standard for connecting agents to tools. If you're building agents without MCP support, you're building for 2024.
What Got Faster
Tool calling went from clumsy JSON wrestling to semantic understanding. Models don't just call functions anymore—they understand what the tools do and chain them intelligently.
Context windows broke through the ceiling. We went from "fit a function" to "fit your entire codebase"—and then immediately asked for more. The irony isn't lost on anyone that we filled 200K tokens the day we got them.
Local inference stopped being a compromise. Running models locally isn't about privacy anymore—it's about latency, cost, and the freedom to experiment without watching your API bill spiral.
What Got Slower
Decision-making. Paradoxically, more capable models made us slower. When an agent might solve the whole problem, you spend more time reviewing its output than you would have spent writing the code yourself. We're still learning where to trust and where to verify.
Iteration cycles. A feature that took three days now takes four—but the fourth day is spent refining prompts and fixing edge cases the model missed. The quality bar moved up, not the speed.
The Tooling That Disappeared
Remember when we needed:
- Complex prompt templates? Now we just describe intent.
- Elaborate context management? The models handle it.
- Custom parsers for structured output? JSON mode and function calling made that obsolete.
The tools we built to work around limitations... the limitations vanished. The workarounds became technical debt overnight.
What Stays True
Despite the churn, some fundamentals held:
- Context is still king—but now it's about what context, not how much.
- Humans still need to validate—AI confidence ≠ AI correctness.
- Simple still beats clever—the best agent workflows are the boring ones.
What I'm Watching Next Quarter
The next six months won't be about bigger models. They'll be about:
- Agent reliability—less "look what it can do," more "look what it won't break"
- Memory that persists—not just within sessions, but across them
- Tool ecosystems maturing—MCP is the npm moment for AI infrastructure
The pace didn't slow down. If anything, the slope got steeper. The only mistake is assuming tomorrow looks like today.
What's changed in your workflow since late 2025? Are you faster, slower, or just differently occupied?
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