Inside AI Software Agents: The Agent Loop, Prompt Caching and Long-Term Memory

Описание: This video peels back the curtain on AI software agents and explains, in plain language, how they actually think, act, and get things done. Instead of hype, you get the engineering that matters: the agent loop, prompt architecture, tool calls, and the tricks engineers use to keep systems fast and memory-efficient.

Key points covered
The agent loop, the four-step engine behind every decision and action, and why a single task can take many repeated turns.
Why an agent is more than just a model, it is a harness that orchestrates the model, tools, environment, and user input.
How prompts are constructed as rich, evolving context that includes system instructions, available tools, environment state, and the user message.
How tool calls become part of the running history, and why every reply makes the prompt longer.
Prompt caching to speed up repeated computations, and why cache misses force expensive recomputation.
The context window problem, the limits of short-term model memory, and conversation compaction as a clever long-term memory trick.
Practical tradeoffs between speed, memory, and correctness, and what this means for trusting agents with more work.

Why this matters for builders, businesses, and creators
You will understand the design decisions shaping real-world agent behavior, what makes some agents feel fast and reliable, and where failures and inefficiencies come from. This helps you design prompts, choose tools, and set realistic expectations for automation in products and workflows.

If you found this useful, like the video, subscribe for more explainers, and tell us in the comments what agent task you would trust to automate next or what topic you want broken down next. Share the video with a colleague who builds with LLMs.

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