Agentic Frameworks Explained: Building a Scalable AI Ecosystem with an LLM Orchestrator in Python
Last Updated on April 17, 2025 by Editorial Team
Author(s): Zain Baquar
Originally published on Towards AI.
A practical guide to creating modular AI agents and orchestrating their interactions for real-world applications
In the realm of Artificial Intelligence, there seems to be a new ‘State of the Art’ every week. From ChatGPT’s ever-evolving model versions to groundbreaking reinforcement-learning techniques implemented in the training of DeepSeek, to the professional coding capabilities of Anthropic’s Claude 3.7 Sonnet — the rapid developments in this field continue to baffle even those who live and breathe data and AI. Talented professionals risk being left behind if they don’t keep up with the latest literature and methodologies behind language models and their applications. Even in my own writings, my hiatus resulted from needing to catch up to a point where I felt comfortable guiding others in their pursuit to build innovative solutions.
In 2025, we have matured from language models that have an almost complete understanding of language to agents that use that understanding to perform dedicated tasks.
To put it simply, agents are applications designed with task-based components, often driven by language models behind the scenes. These task-based components are known as ‘Workflows’. One example of workflow is application that uses LLMs to extract specific information from textual documents and performs some action on that extracted data. Another example is an application that uses… Read the full blog for free on Medium.
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