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Foundations of AI Agents in Python

Course Overview and Agent Paradigms

Setting Up the Python Environment for Agents

Agent Design Patterns and Data Flow

Perception and Memory

Perception Pipelines: Text, Vision, and Tools

Knowledge and Working Memory

Reasoning and Planning

Reasoning with LLMs and Structured Outputs

Planning and Decision-Making

Action and Orchestration

Building the Agent Loop and Tooling

Multi-Agent Systems and Collaboration

Evaluation, Safety, and Deployment

Measuring Agent Performance

Reliability, Safety, and Compliance

Packaging and Deployment

Capstone: End-to-End Agent Build

Capstone Project: Design, Implement, and Evaluate an Autonomous Agent

Knowledge Checks and Feedback

Quiz: Agent Foundations

Quiz: Perception and Memory

Quiz: Reasoning and Planning

Quiz: Action and Orchestration

Quiz: Evaluation and Deployment

Course Feedback Survey

Build real, reliable autonomous AI agents end to end—without the hand-waving. In Building Autonomous AI Agents in Python: Perception to Action, you’ll design and implement agents that can perceive the world, reason over context, plan multi-step work, and act safely in real systems. Using modern Python (3.11+), you’ll master the agent loop: Perception → Memory → Reasoning → Planning → Action. You’ll connect LLMs, vector stores, OCR, and tool adapters to ingest text, images, and APIs; ground decisions with retrieval; and execute actions through robust, auditable tool interfaces. Go beyond prompts into production: observability, testing, guardrails, safety, and deployment with FastAPI and Docker. Learn multi-agent collaboration patterns (coordinator/specialist/critic), concurrency with asyncio, and scaling with queues and workers. Each module is hands-on with reproducible environments, typed schemas, and testable components. The capstone guides you to ship a real autonomous agent—designed, evaluated, and iterated with measurable metrics like success rate, latency, and cost. Whether you’re upgrading chatbots into dependable operators or building AI-native workflows, this course equips you with patterns, code, and confidence to deliver agents that don’t just think—they ship.

Building Autonomous AI Agents in Python: Perception to Action

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            <p>From perception to action, modern AI agents are evolving into software systems that can observe their world, reason with structured context, plan across uncertain terrain, and safely execute real work. This course turns that arc into a practical path you can follow in Python. You will first ground your understanding in agent paradigms—task vs. tool vs. autonomous agents; reactive, deliberative, and hybrid designs; single- and multi-agent systems—while setting up a reproducible Python 3.11+ environment with the core ecosystem you’ll use throughout: numpy, pydantic, asyncio, FastAPI, and model SDKs like OpenAI and Hugging Face. Along the way, you’ll learn design patterns for the Perception → Memory → Reasoning → Planning → Action loop, how to structure state machines and planners, and how to build observability into your work with logging, tracing, and metrics.</p>

<p>The middle of the course focuses on building strong perception and memory. You will implement text and web ingestion with schema validation, add vision/audio hooks such as OCR and captioning, and integrate tool adapters for safe interactions with search, databases, file I/O, and shell commands. You’ll then design knowledge and working memory with embeddings and vector stores (FAISS, Chroma) to power retrieval-augmented generation, plus strategies for memory aging, summarization, and compression. With grounding in place, you’ll move into reasoning and planning: prompt engineering and structured outputs, function/tool calling via JSON schemas, and planning techniques ranging from task decomposition and heuristic search to LLM-driven planners, complete with execution policies, retries, and dead-end detection.</p>

<p>Next, you will orchestrate robust action. You’ll build an async agent loop with cancellations and timeouts, design safe and idempotent tool interfaces, and connect to external systems over REST, databases, and files. Guardrails—permissioning, scopes, dry-run modes, and human-in-the-loop—ensure reliability at scale. The multi-agent module introduces coordinator, specialist, and critic roles, message-passing protocols, shared memory, and scaling patterns with queues, workers, and concurrency. Finally, you’ll measure what matters: success rate, time-to-completion, reliability, and cost. You’ll create replay harnesses, unit and integration tests, golden datasets, and A/B tests, while addressing safety and compliance through prompt-injection defenses, least-privilege credentials, secrets management, monitoring, and incident response.</p>

<p>The course culminates in a capstone where you design, implement, and evaluate an end-to-end autonomous agent for a real task such as research-and-report, data cleanup, or support triage. You will package your agent as a service (FastAPI) or CLI (Typer), manage configuration with pydantic settings and dotenv, and containerize with Docker while balancing GPU/CPU trade-offs and cost-control strategies like caching and model selection. Knowledge checks and a final survey help you consolidate learning and provide feedback. By the end, you’ll have a tested, observable, and deployable Python agent that can perceive, remember, reason, plan, and act—safely and effectively.</p>
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