f you define agents as LLM systems that can perform useful work via tool calls over multiple steps then agents are here and they are proving to be extraordinarily useful. The two breakout categories for agents have been for coding and for search.

It turned out that the real unlock of reasoning was in driving tools. Reasoning models with access to tools can plan out multi-step tasks, execute on them and continue to reason about the results such that they can update their plans to better achieve the desired goal. A notable result is that AI assisted search actually works now. Hooking up search engines to LLMs had questionable results before, but now I find even my more complex research questions can often be answered by GPT-5 Thinking in ChatGPT. Reasoning models are also exceptional at producing and debugging code. The reasoning trick means they can start with an error and step through many different layers of the codebase to find the root cause. I’ve found even the gnarliest of bugs can be diagnosed by a good reasoner with the ability to read and execute code against even large and complex codebases.

some practices that can make those discussions easier, by starting with constraints that even skeptical developers can see the value in: Build tools around verbs, not nouns. Create checkEligibility() or getRecentTickets() instead of getCustomer(). Verbs force you to think about specific actions and naturally limit scope.Talk about minimizing data needs. Before anyone creates an MCP tool, have a discussion about what the smallest piece of data they need to provide for the AI to do its job is and what experiments they can run to figure out what the AI truly needs.Break reads apart from reasoning. Separate data fetching from decision-making when you design your MCP tools. A simple findCustomerId() tool that returns just an ID uses minimal tokens—and might not even need to be an MCP tool at all, if a simple API call will do. Then getCustomerDetailsForRefund(id) pulls only the specific fields needed for that decision. This pattern keeps context focused and makes it obvious when someone’s trying to fetch everything.Dashboard the waste. The best argument against data hoarding is showing the waste. Track the ratio of tokens fetched versus tokens used and display them in an “information radiator” style dashboard that everyone can see. When a tool pulls 5,000 tokens but the AI only references 200 in its answer, everyone can see the problem. Once developers see they’re paying for tokens they never use, they get very interested in fixing it.

Each tool performs a single operation with clearly defined inputs and outputs. Tools may require user consent prior to execution, helping to ensure users maintain control over actions taken by a model.

Tools are model-controlled, meaning AI models can discover and invoke them automatically. However, MCP emphasizes human oversight through several mechanisms. For trust and safety, applications can implement user control through various mechanisms, such as: Displaying available tools in the UI, enabling users to define whether a tool should be made available in specific interactions Approval dialogs for individual tool executions Permission settings for pre-approving certain safe operations Activity logs that show all tool executions with their results