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The Execution Layer Shift We are moving beyond simple prompting into the era of the 'agentic harness'—sophisticated execution layers like Anthropic’s Model Context Protocol (MCP) that wrap models in persistent context and tool-making capabilities.

Efficiency vs. The Token Tax While frontier models like GPT-5.2 solve long-horizon planning drift, developers are fighting a 'token tax' with lazy loading for MCP tools and exploring NVIDIA’s Test-Time Training to bypass the autoregressive tax.

Small Models, Specialized Actions The 'bloated agent' is being replaced by hyper-optimized micro-models and frameworks like smolagents that prioritize transparent Python code and direct GUI control.

Infrastructure Bifurcation As power users hit usage caps on models like Claude Opus 4.5, the ecosystem is splitting between sovereign hardware stacks and hyper-specialized inference engines like Cerebras.

Code-as-Action Dominance: We are moving from fragile JSON schemas to native Python execution via tools like smolagents and Claude Code, enabling agents to manipulate the filesystem and OS directly.

Standardizing the Agentic Web: The rapid adoption of MCP and AGENTS.md v1.1 provides the 'USB port' and behavioral standards required for reliable, enterprise-grade autonomous systems.

Hardware-Native Autonomy: A strategic pivot toward local inference on AMD hardware and Marlin-optimized kernels is slashing latency and proving that the future of agents lives on the edge.

Hardening the Stack: As agents transition to background execution, the focus has shifted to resilience—solving for 429 rate limits and securing zero-click workflows against emerging vulnerabilities.