🟢 How AWS truly changes the way developers build agent pipelines?
This works just fine until the agent encounters a situation you haven't anticipated.
AWS's Strands Agents take a different approach.
Instead of overcomplicating orchestration, they rely on the reasoning capabilities of modern models and allow the model to drive the workflow itself.
You:
- set the goal,
- provide the tools,
- and let the agent figure out how to reach that goal on its own.
From an implementation perspective, everything is as simple as possible » you only need to define three things:
1. LLM,
2. tools,
3. the task.
And that's it. The agent cycle takes over from there.
At launch, the model decides:
- whether a tool is needed,
- which one specifically,
- how to format the input data,
- and when to stop.
All the logic is » model-driven.
That is, instead of rules like "if it's math, call the calculator" or "if the API is down, retry", the model dynamically plans steps and executes them itself.
The workflow is formed during execution, based on the goal and available tools.
A concrete, applied example is shown in the video above.
This is an agent based on MCP, which can create videos in the style of 3Blue1Brown with a single simple prompt.
The setup was minimal:
- an MCP server with a tool for running Manim scripts,
- then we described the agent with these tools. Without system prompts and without workflow rules.
Next, the model itself:
- planned the steps,
- generated a Manim scene,
- and autonomously invoked the necessary tool.
Even a minimal configuration already provides a surprisingly high level of capabilities. And as you add better tools or models, the agent becomes stronger on its own - without rewriting the workflow.
Framework is completely open source and works with any local setup. 📖
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