Short summaries of agent optimization and AI engineering work — harnesses, prompt and context optimization, orchestration, memory, and agentic RL.
Filtering by context engineering. 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 - Self-service analytics fails in two directions: give an agent broad data access and metric definitions diverge across teams; lock access down and it cannot answer the business questions people actually have. - Three specific failure modes drive wrong answers: concept-to-entity ambiguity (mapping a question to the...
1. Problems / challenges / motivations - Retrieval-style memory injects a blob of past experience ahead of time, most of which is irrelevant to the current step. - Irrelevant memory costs tokens, adds latency, and actively distracts the policy. - Vector recall quality is not the same thing as memory being useful to an acting agent. 2. Key ideas - Replaces...
1. Problems / challenges / motivations - Rewriting a whole system prompt on each optimization step introduces brevity bias: the rewrite compresses away details it does not currently see a use for. - Repeated rewrites cause context collapse, where accumulated knowledge degrades rather than compounds. - Adapting a system to a new domain by retraining is slow...
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...
1. Problems / challenges / motivations - Long context windows are marketed as capacity, and teams treat "fits in the window" as "will be used correctly." - Needle-in-a-haystack tests use high semantic similarity between question and needle, which is the easy case. - Without evidence of degradation, there is no case for the engineering cost of compaction and...