Simor Consulting
Category: Agent Orchestration
Every AI agent system eventually faces the same problem. You have built a capable language model. You want it to interact with your tools, your data, your APIs. So you write a custom integration layer
Google announced the Agent-to-Agent protocol, A2A, as a standard for how AI agents communicate with each other. This sits alongside the Model Context Protocol, MCP, which standardises how agents acces
A single agent can already fail in difficult ways: a tool times out after applying a change, retrieved evidence is stale, or an answer invents a policy. With one decision loop, there are fewer handoff
The Model Context Protocol gives AI agents a standardised way to discover and invoke external tools. In development, MCP works well with a local server running on localhost and a handful of tools. The
When an AI agent can call external tools, the security boundary shifts from the model to the tool layer. The model generates a request to call a tool. The tool executes against real systems, reading d
A mid-size automotive parts manufacturer with operations spanning 15 countries and relationships with over 200 suppliers faced a supply chain coordination problem that was consuming too much of their
A language model that only generates text is not enough for most enterprise problems. The real value emerges when an AI system can look up your customer record, check inventory levels across warehouse
A software debugging agent receives a bug report. It needs to search code, understand the error, propose a fix, write tests, and summarise for the developer. None of these steps are independent. Each