@klawikowski

The agentic landscape is undergoing a fundamental architectural purge. We are moving past the 'wrapper era' of 2024, characterized by brittle JSON schemas and heavy abstractions, toward a leaner, more recursive future. Meta’s $500M acquisition of Manus AI serves as a definitive signal: general-purpose agentic architectures are being pulled into the platform layer to solve the long-standing 'hallucination gap.' For builders, the transition is visible in the shift from static prompt engineering to Recursive Language Models (RLMs) and the 'Code Agent' movement led by frameworks like smolagents. By allowing agents to write and execute their own Python logic rather than fighting rigid schemas, we are seeing massive gains in task reliability and context management. Anthropic’s Opus 4.5, with its 64k reasoning window, is facilitating a new hierarchical workflow—using high-reasoning models for planning while smaller, local models handle execution via optimized inference forks like ik_llama.cpp. Whether it's the standardization of the Model Context Protocol (MCP) or the emergence of local-first agent harnesses, the goal is clear: moving agents out of the demo trap and into production-ready autonomy. If you aren't architecting for long-horizon, inspectable reasoning chains, you are building on a foundation that is rapidly being deprecated.

The agentic landscape is undergoing a fundamental shift: we are moving past the chatbot era and into the age of the Agentic Operating System. This week’s developments across the ecosystem signal a massive consolidation of effort around execution and infrastructure. Meta’s multi-billion dollar bet on Manus AI confirms that the market is prioritizing autonomous action over simple generation. Meanwhile, Hugging Face is proving that the path to higher reasoning isn't through more rigid schemas, but through Code-as-Actions—letting agents write and execute Python to solve complex logic that JSON-based tool calling simply cannot touch. Efficiency is the new north star. Whether it’s Anthropic’s Claude Code prioritizing a skills architecture for token economy or builders optimizing local ROCm kernels for 120B+ parameter models, the goal is clear: low-latency, high-precision autonomy. However, infrastructure alone isn't a silver bullet. Even with persistent memory via Mem0 and secure sandboxing through E2B, agents are hitting a planning wall on benchmarks like GAIA. The challenge for today’s practitioner is no longer just prompt engineering; it’s architecting the stateful, code-native environments where agents can fail, iterate, and eventually succeed.