Independent AI analysis
Source-grounded writing on agent protocols, model evaluation and the engineering behind production AI. Every article states its answer up front and names every source it used.
robots.txt, llms.txt, Content-Signal and AIPREF all describe what an agent may fetch. The 2,000 packages uploaded to RubyGems in May went through a signup form and a publish endpoint, which none of those files govern. Read controls are advisory. Write paths need identity and rate limits.
3 min read
MCP revision 2026-07-28 requires ttlMs and cacheScope on every list result and tells servers to return tools in a deterministic order. Both are filed as minor changes. They are the most direct cost change in the revision, because tool definitions sit in the cacheable prefix of every agent step.
3 min read
SEP-2640 reached Final status and landed in the MCP docs on 13 September 2026. It requires hosts to verify every skill file against a SHA-256 digest and byte size, and binds approval to that manifest so one changed file revokes it. The tools primitive specifies none of this. MCP now has two content surfaces with opposite integrity models.
3 min read
Anthropic measured one Drive to Salesforce transfer falling from 150,000 tokens to 2,000 by writing code against MCP servers instead of calling tools directly. That is a code execution result rather than an argument for deleting MCP. The Agent Skills spec puts numbers on the alternative: about 100 tokens per skill at rest.
4 min read
Prompt caching stores a prefix of your prompt so later requests reuse it instead of reprocessing it. Reads are far cheaper than base input tokens, but writing the cache costs a premium and entries expire. It pays whenever a large stable prefix is reused several times inside the TTL, and loses on one-shot traffic.
5 min read
A large context window raises the ceiling on how much a model can see at once, but accuracy is not uniform across that window. Retrieval quality, latency and cost still dominate real workloads. Long context is best treated as a way to stop chunking small documents rather than as a replacement for retrieval.
5 min read
The Model Context Protocol is a JSON-RPC 2.0 layer that lets an AI client discover and call capabilities exposed by separate servers. It defines three primitives: tools, resources and prompts. Since revision 2026-07-28 it is stateless, negotiating per request rather than per session. It standardises transport and discovery. Authorisation stays your job.
6 min read