While exploring AI agents, I realized the hardest part isn't calling an LLM.
It's deciding when to reason, use a tool, or ask for more context.
Real AI systems aren't just:
User → LLM → Response
They're:
Understand → Decide → Act → Validate → Respond
I've been building AI agents lately and realized building one is the easy part.
The real challenge is making them reliable handling hallucinations, tool failures, infinite loops, memory, and unexpected decisions.
What's the hardest problem you've faced while building AI agents?
just moved from answering health questions to sitting inside the doctor's chart. Read-only, HIPAA-aligned, 99% safety-rated in testing solid guardrails. Now the real test: does trust keep pace with how fast this scales?
Today, we’re bringing ChatGPT closer to the systems, information, and workflows healthcare teams already rely on. ♥️
We’re introducing a new EHR integration to connect supported Epic environments to ChatGPT and a plugin connecting to nine additional industry data sources.
Fable 5.1 is now live in Claude Code and the Claude Platform.
It's priced the same as Fable 5, with 75% cheaper API cache reads. It gets a lot further into a long task before it needs your input, is better at telling you when it's stuck, and its writing style is more natural.
Every site is throwing up walls against AI right now bot detection, rate limits, API paywalls. Vibe coding used to mean just hit the site. Now it means find the door that's still open. The arms race between AI agents and antibot defenses is the real issue in 2026, not the models.