The list is on point, but the deeper point is that almost every hyped agent framework tried to hide complexity instead of confronting it.
The ones who win are those who treat agents as supervised, evaluated, and tightly constrained systems, not autonomous magic.
Andrej Karpathy: "90% of what AI twitter tells you to learn will be dead in 6 months"
Here are 10 things senior AI engineers stopped wasting time on:
1. AutoGen / AG2: moved to community maintenance, releases stalled. dead for production
2. CrewAI: demos well, breaks in production. engineers building real systems already moved off it
3. Autonomous agent pitches: the AutoGPT / BabyAGI wave is dead in product form. the industry settled on supervised, bounded, evaluated agents
4. Agent app stores / marketplaces: promised since 2023, zero enterprise traction
5. SWE-bench leaderboard chasing: researchers proved nearly every public benchmark can be gamed without solving the underlying task
6. Microsoft Semantic Kernel: unless you're locked into Microsoft enterprise stack, it's not where the ecosystem is heading
7. DSPy: philosophical merit, niche audience. not a general agent framework
8. Horizontal "build any agent" platforms: Google Agentspace, AWS Bedrock Agents, Copilot Studio. confusing, slow-shipping, the math still favors building yourself
9. Per-seat SaaS pricing for agent products: market moved to outcome-based. per-seat is already dead
10. The framework that went viral on HN this week: wait 6 months. if it still matters, it'll be obvious
what actually compounds instead:
- context engineering
- tool design
- orchestrator-subagent pattern
- eval discipline
- the harness mindset (harness > model, always)
- MCP as the protocol layer
be few steps ahead than your competitors and outperform this market till it became mass-opinion
study this.