Your team connected Slack, Jira and GitHub over MCP, and engineers still spend their time steering agents to the right answer. This article looks at why: connectors fetch from one tool at a time, so every agent stitches the pieces together itself, trusts the first doc that looks right, and adds a search to your token bill each time.
Read what changes when a context engine sits on top of your connectors: the same coding task finished 83% faster at 48% lower cost.
Anthropic has recently reached a point where coding isn’t the bottleneck for them. Claude is able to do a lot of heavy lifting. It can iterate on research loops. Or optimize complex programs. Or even build new services from scratch.
However, the main problem Anthropic faces is that the infrastructure they have around writing code is not able to keep up with the amount of code being generated by its agents. Activities like downloading repositories, preparing workspaces, and recording changes are becoming major bottlenecks.
Source: ByteByteGo (system design interviews) — Read the full article on the original site ↗