SumatoSoft

How AI agents talk to each other (and why MCP wasn't enough) 🗣️🤖

MCP solved how an AI agent calls a tool. A separate problem has been getting attention in 2025 and 2026: how AI agents work with other AI agents. The protocol that's emerging here is called A2A, originally proposed by Google and now joined by Cisco, ServiceNow, SAP, and a growing list of enterprise vendors. 🔄

The 2026 deployments increasingly involve several agents collaborating: a planner agent that breaks down a goal, executor agents that handle individual steps, and a verifier agent that checks the output before it commits. That topology needs a protocol. 🧠⚙️

What A2A specifies: discovery (one agent finding others), capability advertisement (what an agent can do), task negotiation (handing off work), and outcome reporting (telling the requester what happened). It sits one layer above MCP, which deals with agent-to-tool calls. 📡

For most enterprises this isn't urgent yet. Single-agent deployments are still where the bulk of production AI lives. The architectural decision worth making now is whether your agent framework supports A2A as it matures, or whether you're betting on a vendor's proprietary multi-agent layer that locks you in the same way orchestration layers do. 🔒🏗️

What affects AI development cost in 2026 💰

AI budgets are shaped by more than model choice.
In our latest article, we break down the main drivers: project complexity, data quality and labeling, privacy and compliance needs, infrastructure choices, third-party model usage, and ongoing spend after launch. 🔧
The article also notes that simple AI systems can start at 5000 to 50000, while enterprise-grade solutions can reach 400000 to 1M+, with ongoing costs often adding 17 to 30% per year. 📈

🤖