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Foundations of Python for AI Agents

Python Essentials for Agentic Systems

Asynchronous and Concurrent Patterns in Python

Agent Architecture and Core Components

Designing Agent Architectures and Interfaces

Reasoning, Planning, and Tool Use

Memory, Knowledge, and Retrieval

Perception, Language Models, and Tools

Integrating Language Models and Multimodal Perception

Environment Interaction and Tooling

Reliability, Observability, and Evaluation

Testing, Evaluation, and Benchmarks for Agents

Telemetry, Traces, and Experimentation

End-to-End Agent Systems and Deployment

Composing End-to-End Agent Workflows

Packaging, APIs, and Productionization

Applied Projects with Examples

Project 1: Web Research and Reporting Agent

Project 2: Workflow Automation Agent for CSV Ops

Knowledge Checks and Quizzes

Quiz: Python and Async Foundations

Quiz: Agent Architecture and Patterns

Quiz: Reasoning and Planning

Quiz: Tool Use and Safety

Quiz: Memory and Retrieval

Quiz: LLM Integration and Multimodality

Quiz: Testing and Evaluation

Quiz: Deployment and Operations

Final Survey

Course Feedback Survey

Build production-ready AI agents in Python from the ground up. This hands-on course guides you through the full lifecycle of modern agentic systems: from Python async foundations and robust architecture patterns to LLM integration, memory and retrieval, observability, and deployment. You will design modular agents that perceive, reason, plan, and act using safe tool interfaces, and you will learn how to evaluate their performance with rigorous tests, benchmarks, and telemetry. Along the way, you will implement end-to-end workflows that orchestrate perception, planning, and tools; add retrieval-augmented memory; and ship your agents as containerized services with cost controls and health checks. The curriculum emphasizes practical engineering trade-offs—concurrency versus parallelism, streaming versus batching, and caching versus freshness—so your agents are not only intelligent but also reliable, scalable, and maintainable. By the end, you’ll have two portfolio-ready projects (a web research/reporting agent and a CSV workflow automation agent), a reproducible benchmark harness, and a deployment blueprint you can adapt to your own organization. If you’re ready to move beyond toy demos and deliver robust AI agents in real environments, this course is for you.

Building AI Agents in Python: Design, Integration, and Operations

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            <p>Building AI Agents in Python: Design, Integration, and Operations provides a rigorous, end-to-end journey from Python foundations to production-grade agent systems. The course begins by establishing a precise technical baseline: modern Python (3.11+) setup, packaging with pyproject.toml, dependency management, logging and configuration via pydantic or dotenv, and robust serialization. You will refresh core language features—typing, dataclasses, context managers—before moving into asynchronous programming with asyncio, resource management for I/O, environment-driven configuration, documentation scaffolding, and CLI construction. A dedicated module on concurrent and asynchronous patterns deepens this groundwork with event loops, task orchestration, backpressure, timeouts, retries, and cancellation, culminating in a practical async task runner for tool scheduling.</p>
<p>With these foundations, the curriculum advances to agent architecture and its core abstractions: Agent, Tool, Memory, Policy, and Environment. You will study canonical agent loops—perceive–reason–plan–act, ReAct, and toolformer-like flows—while designing message schemas, state containers, and event buses for modularity. Reasoning and planning strategies are addressed through prompt/program synthesis, chain-of-thought, search-based planners, and task decomposition, paired with tool adapters for HTTP, databases, filesystems, and function-calling models. The memory track distinguishes short-term versus long-term stores, introduces embeddings and vector databases for retrieval augmentation, and covers summarization and pruning. A practical emphasis continues with hands-on builds: a minimal pluggable Agent, a tool-using planner, and a retrieval-augmented memory layer.</p>
<p>The integration section focuses on language models and multimodal perception, including LLM client management, rate limits, batching, streaming tokens, and schema-validated function calling. You will implement JSON-schema-driven tool invocation and construct perception pipelines for text, image, and audio inputs. Tooling and environment interaction prioritize safety: sandboxed execution, permissioning, file I/O, HTTP clients, browser automation, and defensive engineering through guardrails, error handling, and red-teaming. Reliability, observability, and evaluation are treated as first-class concerns, covering unit and scenario tests for stochastic behavior, golden traces and mock LLMs for repeatability, benchmark design with confidence intervals, and OpenTelemetry-style tracing, metrics, and experiment tracking for rigorous iteration and incident response.</p>
<p>The final stages synthesize these components into deployable systems. You will orchestrate perception, planning, memory, and tools into resilient pipelines with state persistence, checkpointing, and resumable execution, then scale using queue-based workers and schedulers. Productionization modules address service interfaces (REST, WebSocket, CLI), containerization, dependency pinning, secrets management, cost control, caching, and multi-provider fallbacks, culminating in an agent service with health checks and quotas. Applied projects—such as a web research and reporting agent and a workflow automation agent for CSV operations—translate principles into measurable outcomes with factuality checks, coverage metrics, latency and throughput targets, and reproducible repositories. Knowledge checks and a final survey ensure comprehension, reflection, and continuous improvement, equipping practitioners to design, evaluate, and operate robust AI agents in real-world environments.</p>
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