Short summaries of agent optimization and AI engineering work — harnesses, prompt and context optimization, orchestration, memory, and agentic RL.
Filtering by harnesses. Clear filter.
1. Problems / challenges / motivations - Discussion of recursive self-improvement defaults to model weights, while most of the practical gains in 2026 came from the code around the model. - The relevant literature is scattered across context engineering, scaffold search, and Gödel-machine work with no common map. - Practitioners have no shared vocabulary...
1. Problems / challenges / motivations - IDE-centric tooling assumes a human in the editor, which is the wrong center of gravity once agents do most of the editing. - Steering long-running agents needs surfaces for both synchronous and asynchronous work. - Model and harness are usually developed by different teams and integrated late. 2. Key ideas - A...
1. Problems / challenges / motivations - Codebases are written to be legible to humans, which is not the same as being legible to a coding agent. - When agents write most of the code, the bottleneck moves from writing to specifying, validating, and observing. - Agent failures in a large repo are hard to diagnose without per-run observability. 2. Key...
1. Problems / challenges / motivations - Long-horizon agent work needs both strong coding and strong reasoning, which had been split across separate models. - Fire-and-forget agent runs waste time when the user can see it going wrong but cannot intervene. - Prompting guidance for agent models is under-specified relative to how much it changes behavior. 2....
1. Problems / challenges / motivations - Building a specialized agent per domain multiplies maintenance and fragments capability. - Stuffing all domain knowledge into a system prompt does not scale past a few domains. - Reusable agent capability had no portable packaging format. 2. Key ideas - Explains why Anthropic stopped building specialized agents and...
1. Problems / challenges / motivations - Loading every MCP tool definition into context burns tokens before the agent does anything. - Intermediate tool results flow through the model even when the model only needs a filtered summary. - Tool selection degrades as the tool count grows. 2. Key ideas - Presents MCP servers as code APIs the agent explores on a...
1. Problems / challenges / motivations - Prompt engineering assumes a fixed instruction; agents assemble a different context at every step. - Long-horizon agents run past any context window, so something has to decide what survives. - The field lacked shared vocabulary for what that decision process is. 2. Key ideas - Frames context engineering as the...