It feels like we're in the early mobile years where companies have not figured out what to do with this new technology. I hope the Uber and Candy Crushes of the AI era will land in 2026! (well maybe not candy crush, but some IA native games would be nice)
This is indirect prompt injection through the observation channel rather than through user input. Read-only access and invocation logging both assume the threat arrives from outside the pipeline. When the observed data itself is the attack surface, you need output sanitization or context sandboxing before telemetry reaches the model. Multi-tenant or production environments where the MCP server traces workloads from multiple teams would be particularly exposed.
Let the LLM work in code mode. Don't make it have to be the execution engine too. It can do it but it's slow and giving it tools script what it wants will go far better.
I do think there's an interesting possibility where we turn MCP into something composable. Capnproto has promise pipelining where you can issue new instructions with results you don't have yet. If MCP could copy those tricks, & express promises... and those promises worked across MCP servers ("third party handoff", https://github.com/capnproto/go-capnp/issues/597)... you'd start to have something as compellingly composable as the shell.
There are a quite a few startups created by connecting relevant eBPF/OTel traces e.g. in response to uncaught exceptions (traditional RAG-based bug-fix generation).
OTEL is the common denominator for all the observability purposes including the ones that come from eBPF and the application layer.
Same architectural idea as Ingero. We skip the aggregation layer. We also skip the MCP part. The agent uses a CLI instead, which keeps it composable with any coding agent workflow.
You can have your claude poke at the logs/metrics/traces using a dedicated cli tool paired with some agent skills. It is Apache-licensed and you can try it locally.