agent capabilities
Beyond Market Intelligence keeps agent capabilities in one place: 3 stories so far. The section currently leads with “Designing the System That Lets AI Models Deliver Real Features”, “Meet your new AI assistant that manages emails, accounts, and tasks for you.”, and “Exploring how KV cache transforms LLM inference into an interactive runtime”. An AI coding agent can write a function in seconds, but that's the easy part. Instinct now lives in your inbox, and that is a bigger step than it sounds. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every agent capabilities story on Beyond Market Intelligence, newest first.
Designing the System That Lets AI Models Deliver Real Features
An AI coding agent can write a function in seconds, but that's the easy part. Building a real feature requires understanding the project, finding the right files, using tools safely, and verifying the changes work. A model alone sees only text, it needs a system around it. This is the practical work of harness engineering. For a deeper look at what happens when that system falters, see our related piece on how agentic AI demands smarter governance to tame silent failures.
Meet your new AI assistant that manages emails, accounts, and tasks for you.
Instinct now lives in your inbox, and that is a bigger step than it sounds. The AI agent can create accounts, reach out to businesses, and handle support requests without you lifting a finger. We like this move because it shifts the assistant from a reactive tool into a proactive operator. It is practical, direct, and genuinely useful. For more on how personal agents are scaling, check out "Meta Accelerates Muse's Growth with Expanded Promotion." The momentum across the space is worth watching.
Exploring how KV cache transforms LLM inference into an interactive runtime
Most LLM agents feel reactive because they move token by token, pausing for the model to catch up. Our team has been exploring a different path: modifying the model's inference state, the KV cache, to make it a true runtime. This approach, outlined in a post by our researchers, powers more interactive systems. We see this as a key axis for agent capability, sitting between costly model changes and abstract harnesses.