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Your AI agent has a blind spot (Sponsored)
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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 can affect autonomous vehicles. These signals shape decisions, yet most AI systems can’t observe them.
But context is only as valuable as the data behind it. Enterprise applications need trusted, real-time weather intelligence—not just generic forecasts. Signals like lightning activity, road conditions, hail risk, and weather impacts often matter more than temperature alone.
Xweather’s MCP-ready weather API gives AI agents access to trusted weather intelligence and the real-world context behind weather-driven decisions. Learn how it works in this technical guide.
This week’s system design refresher:
MCP vs A2A vs ACP: How AI Agents Actually Talk to Each Other
8 Frontier Open Models I’m Most Excited About
LLM vs RAG vs Agent evals
How Distributed Tracing Works at the High Level?
The Life of a Redis Query
MCP vs A2A vs ACP: How AI Agents Actually Talk to Each Other
Agents are capable on their own. Combined with tools and other agents, their capabilities compound. But how should they communicate?
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MCP: agent to tool communication.
The host app receives the user request, its embedded MCP client formats and routes it to the right MCP server, the server executes the tool call and returns a structured response. The agent uses the result to continue reasoning.A2A: agent to agent communication.
An agent that can't complete a task alone discovers a capable peer via its Agent Card (published at a well-known URL), delegates the task, and receives a structured result back. If the second agent needs more input mid-task, it pauses in the input-required state and loops back to the first.ACP: agent to agent communication over REST (merged into A2A).
ACP took a REST-first approach. Peers were discovered through an Agent Manifest, called directly over HTTP, and responded to sync for low-latency tasks or via async SSE stream.
In production, MCP and A2A are complementary. MCP handles tool access, A2A handles agent communication.
Are you running MCP and A2A together in your agent stack?
Introducing Attio: the agentic CRM. (Sponsored)
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Attio, the agentic CRM, makes it incredibly easy for anyone to run workflows for any GTM play they need.
Describe what you want, and Attio builds it. I just built a workflow that runs every morning, surfaces the deals that need my attention today, like anything with a stage change or a new signal in the last 24 hours.
Hundreds of thousands of automations already run on Attio every day. Ready to try now?
LLM vs RAG vs Agent evals
LLMs, RAG pipelines, and agents are different systems, but the recipe for evaluating them is the same: pick a task, collect eval data, develop a grader.
The diagram below breaks it down across four popular AI systems.