As AI agents take on decision‑making, they need awareness of real‑world conditions. A weather delay can reroute deliveries. Lightning can pause field operations. Road conditions affect autonomous vehicles. These signals shape decisions, yet most systems can’t observe them. Xweather’s MCP‑ready weather API gives agents trusted, real‑time weather intelligence and context behind weather‑driven decisions.

MCP, A2A, and ACP define how agents talk to each other. MCP is agent‑to‑tool communication: the host app routes a request to an MCP server, executes the tool call, and returns a structured response that the agent uses to continue reasoning. A2A is agent‑to‑agent communication via an Agent Card; ACP is a REST‑first approach merged into A2A.

In production, MCP and A2A are complementary. MCP handles tool access while A2A handles agent communication. Attio, an agentic CRM, lets anyone run workflows for any GTM play. I built a workflow that surfaces deals needing attention each morning, using the agent stack to surface signals in the last 24 hours.

Evaluating these systems involves grading generation, retrieval, and coordination. LLMs are judged by an LLM‑as‑judge; RAG pipelines assess retrieval and generation; multi‑agent systems blend code tests, LLM‑as‑judge, and human review. Each component introduces new failure points that the evaluation must catch.