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How to Put a Hard Spending Limit on AI Agents: Token Budgets, Tool Budgets, and Kill Switches
AI agents are fundamentally different from ordinary chatbot requests. A normal API request might look like: An agent can look more like: One user action can therefore trigger: That creates a new engineering problem: How do we guarantee that one agent run cannot spend more than we are willing to pay? Simply checking the bill…
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Build Long-Term Memory for AI Agents with Redis, Vector Search, and Context Compaction
Large language models do not naturally remember yesterday’s conversation. Every model request begins with the context your application sends with that request. That creates a problem for AI agents expected to work across: A basic chatbot can keep sending the entire conversation back to the model. An actual agent eventually cannot. The context becomes: A…
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MCP vs. A2A in Practice: Build a Multi-Agent System Where MCP Handles Tools and A2A Handles Agent Communication
MCP and A2A are often discussed as if developers need to choose one. You usually do not. They solve different problems. A useful way to remember the distinction is: Suppose we are building an AI incident-response system. A coordinator receives: The coordinator does not need direct access to every monitoring database, deployment system, ticketing platform,…
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Understand Technology. Discover Better Tools. Work Smarter. Practical guides, software reviews, comparisons, and tutorials covering AI, SaaS, automation, data, cloud, and modern technology. Explore Digital Stackroom Explore practical guides, tutorials, reviews, and comparisons across the technologies shaping modern work and business. AI & Machine Learning Understand AI, discover useful tools, and learn practical ways to…