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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 AI agents you can trust in production. “Building Production-Ready AI Agents in Python: Design to Ops” takes you from core Python patterns to shipping reliable agent services. You’ll learn modern async fundamentals, agent architecture (policy, memory, tools, environment), and the practical mechanics of integrating LLMs, retrieval, and safe tool execution. Then you’ll operationalize: testing stochastic systems, telemetry and traces, benchmarking, and cost control. The course is hands-on: each section includes guided builds—from an async task runner to retrieval-augmented memory and a sandboxed file-edit tool—culminating in deployable agent services. By the end, you’ll compose end-to-end workflows, package APIs, containerize, and ship with health checks, quotas, and observability. Designed for Python developers, data/ML engineers, and builders who need agents that are robust, auditable, and performant, this program emphasizes reproducibility, structured configs, and fault tolerance so your agents can scale from laptop to production with confidence.

Building Production-Ready AI Agents in Python: Design to Ops

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            <p>Building production-ready AI agents demands more than prompt engineering—it requires disciplined software design, rigorous reliability practices, and a deep command of Python’s modern ecosystem. This course systematically advances from core Python capabilities through agentic architectural patterns to operational deployment. You will establish a solid foundation in Python 3.11+, mastering packaging with pyproject.toml, dependency pinning, configuration management, structured logging, and serialization formats crucial for traceable systems. Equal emphasis is placed on concurrency: event loops, asyncio primitives, backpressure, cancellation, and robust retry semantics, culminating in an async task runner that orchestrates tool calls with predictable performance characteristics.</p>

<p>With the execution substrate in place, the curriculum formalizes agent design around clear interfaces and message schemas: Agent, Tool, Memory, Policy, and Environment abstractions. You will implement perceive–reason–plan–act loops, ReAct-style flows, and toolformer-like patterns, then extend them with planners, tool adapters (HTTP, databases, filesystems, function-calling models), and retrieval-augmented memory spanning episodic and semantic stores. Practical integration of language models covers rate limiting, batching, streaming tokens, and JSON-schema-based tool calling, complemented by safe sandboxes, permissioning, and guardrails for environment interaction. Reliability and observability are treated as first-class concerns—unit, integration, and scenario tests for stochastic behavior; golden traces and mock LLMs for repeatability; benchmarks with confidence intervals; and OpenTelemetry-style traces for step-level introspection and incident response. The course concludes with end-to-end composition, checkpointing, workers and schedulers for scale, and productionization via APIs, containers, secrets, cost control, caching, and multi-provider fallbacks. Applied projects—web research/reporting and CSV workflow automation—translate theory into reproducible, testable deliverables, with knowledge checks and a final survey ensuring retention and actionable feedback.</p>
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