NEW SPECIALIZATION

Building Next-Gen AI Solutions
with Agents

Learn to design and deploy intelligent multi-agent systems that leverage LLMs to solve real-world challenges at scale.

Enroll now

ELEVATE YOUR EXPERTISE

ELEVATE YOUR EXPERTISE

ELEVATE YOUR EXPERTISE

About the specialization

About the specialization
About the specialization

This specialization will guide you through the practical development of LLM-powered agents, from basic prompt engineering to production-level multi-agent systems. You will build hands-on projects, explore core architectures (RAG, memory/state management, multi-agent collaboration), and learn how to deploy robust agent solutions at scale.

By the end of this program, you will:

Understand how to incorporate external data sources into LLMs.
Build agents capable of complex reasoning and tool usage.
Manage memory, state, and multi-agent workflows effectively.
Deploy and monitor agent systems in production environments.

This specialization will guide you through the practical development of LLM-powered agents, from basic prompt engineering to production-level multi-agent systems. You will build hands-on projects, explore core architectures (RAG, memory/state management, multi-agent collaboration), and learn how to deploy robust agent solutions at scale.

By the end of this program, you will:

Understand how to incorporate external data sources into LLMs.
Build agents capable of complex reasoning and tool usage.
Manage memory, state, and multi-agent workflows effectively.
Deploy and monitor agent systems in production environments.

This specialization will guide you through the practical development of LLM-powered agents, from basic prompt engineering to production-level multi-agent systems. You will build hands-on projects, explore core architectures (RAG, memory/state management, multi-agent collaboration), and learn how to deploy robust agent solutions at scale.

By the end of this program, you will:

Understand how to incorporate external data sources into LLMs.
Build agents capable of complex reasoning and tool usage.
Manage memory, state, and multi-agent workflows effectively.
Deploy and monitor agent systems in production environments.

Mastering the Coding interview

NEW SPECIALIZATION

Building Next-Gen AI
Solutions with Agents

June 3rd, 2025

Learn to design and deploy intelligent multi-agent systems that leverage LLMs to solve real-world challenges at scale.

Enroll now

Mastering the Coding interview

NEW SPECIALIZATION

Building Next-Gen AI
Solutions with Agents

June 3rd, 2025

Learn to design and deploy intelligent multi-agent systems that leverage LLMs to solve real-world challenges at scale.

Enroll now

Mastering the Coding interview

NEW SPECIALIZATION

Building Next-Gen
AI Solutions with
Agents

June 3rd, 2025

Learn to design and deploy intelligent multi-agent systems that leverage LLMs to solve real-world challenges at scale.

Enroll now

Mastering the Coding interview

NEW SPECIALIZATION

Building Next-Gen
AI Solutions with
Agents

June 3rd, 2025

Learn to design and deploy intelligent multi-agent systems that leverage LLMs to solve real-world challenges at scale.

Enroll now

HIGH-LEVEL CONTENT OVERVIEW

HIGH-LEVEL CONTENT OVERVIEW

Program content

Program content

Maser the Coding interview.

01
01

From Prompts to RAG

From Prompts to RAG

  • Prompt-only models limitations

  • What is RAG?

  • Vector databases

  • Retrieval optimization for accurate result

  • Building a basic support agent

  • Prompt-only models limitations

  • What is RAG?

  • Vector databases

  • Retrieval optimization for accurate result

  • Building a basic support agent

02

From RAG to Agents

  • Why Agents?

  • Taxonomy of an Agent

  • Profiling

  • Memory

  • Planning

  • Actions

02

From RAG to Agents

  • Why Agents?

  • Taxonomy of an Agent

  • Profiling

  • Memory

  • Planning

  • Actions

Key Topics & Concepts

Dynamic Data: Prices, product reviews, or any data that frequently changes.
Why Agents?
• Multi-step reasoning about tradeoffs.
• Ability to integrate new information on the fly.
Agent Architecture:
• Orchestrating retrieval, transformation, and final answers.

03
03

Tools & LangGraph

Tools & LangGraph

  • Introduction to LangGraph, Graph Structure: nodes, edges and state management

  • Tool Usage: Searching external APIs, analyzing data, formatting outputs.

  • LLM integrations: Model Context Protocol (MCP)

  • Introduction to LangGraph, Graph Structure: nodes, edges and state management

  • Tool Usage: Searching external APIs, analyzing data, formatting outputs.

  • LLM integrations: Model Context Protocol (MCP)

04

Memory & State Management

  • Memory Architectures

  • Working Memory

  • Long-Term Memory

  • Implementing a Meeting Assistant Agent

04

Memory & State Management

  • Memory Architectures

  • Working Memory

  • Long-Term Memory

  • Implementing a Meeting Assistant Agent

Key Topics & Concepts

Graph Structure
• Nodes: Distinct operations (e.g., web search, analysis).
• Edges: Flow between operations, including conditional
paths.
• State Management: TypedDict usage across nodes.
Tool Usage:
• Searching external APIs, analyzing data, formatting outputs.
LangGraph:
• Building explicit workflows with maintainable state transitions.

05
05

Multi-Agent Systems

Multi-Agent Systems

  • Multi-Agent Architecture

  • Networked Agents

  • Supervisor/Worker Model

  • Collaboration & State Sharing

  • Multi-Agent Architecture

  • Networked Agents

  • Supervisor/Worker Model

  • Collaboration & State Sharing

06

Advanced Agent Patterns

  • Router Pattern

  • Parallelization Strategies

  • Human-in-the-Loop

06

Advanced Agent Patterns

  • Router Patter

  • Parallelization Strategies

  • Human-in-the-Loop

Key Topics & Concepts

Router Pattern:
• Intelligent classification of tasks (e.g., ticket routing).
• Using LLMs to decide how tasks are distributed.
Parallelization Strategies
• Map-reduce approach for handling large batches.
• Managing concurrency and collating results.
Human-in-the-Loop
• When to pause for manual decisions or validation.
• Resuming flows with updated state.

07
07

Agent Frameworks

Agent Frameworks

  • LangGraph

  • AutoGen

  • Smolagents

  • Framework Selection: Trade-offs and considerations

  • LangGraph

  • AutoGen

  • Smolagents

  • Framework Selection: Trade-offs and considerations

08

Production Deployment

  • Production Architecture

  • Monitoring & Cost Control

  • Security Considerations

08

Production Deployment

  • Production Architecture

  • Monitoring & Cost Control

  • Security Considerations

Key Topics & Concepts

Production Architecture
 • Queue-based processing, load balancing.
• Ensuring consistent state across instances.
Monitoring & Cost Control
• Tracking usage costs per request.
• Performance metrics and error handling.
Security Considerations
• Rate limiting, input validation, and access control.
• Handling sensitive data with caution.

Final Project
Final Project

AI Shopping Assistant

AI Shopping Assistant

Design and build a conversational agent capable of handling both sales and customer support flows, including semantic and structured product search, cart management, agent routing, and human escalation, all integrated via tool use and memory.

Design and build a conversational agent capable of handling both sales and customer support flows, including semantic and structured product search, cart management, agent routing, and human escalation, all integrated via tool use and memory.

Enroll now

Enroll now

HIGH-LEVEL CONTENT OVERVIEW

HIGH-LEVEL CONTENT OVERVIEW

Program content

01

From Prompts to RAG

  • Prompt-only models limitations

  • What is RAG?

  • Vector databases

  • Retrieval optimization for accurate result

  • Building a basic support agent

02

From RAG to Agents

  • Why Agents?

  • Taxonomy of an Agent

  • Profiling

  • Memory

  • Planning

  • Actions

03

Tools & LangGraph

  • Introduction to LangGraph, Graph Structure: nodes, edges and state management

  • Tool Usage: Searching external APIs, analyzing data, formatting outputs.

  • LLM integrations: Model Context Protocol (MCP)

04

Memory & State Management

  • Memory Architectures

  • Working Memory

  • Long-Term Memory

  • Implementing a Meeting Assistant Agent

05

Multi-Agent Systems

  • Multi-Agent Architecture

  • Networked Agents

  • Supervisor/Worker Model

  • Collaboration & State Sharing

06

Advanced Agent Patterns

  • Router Patter

  • Parallelization Strategies

  • Human-in-the-Loop

07

Agent Frameworks

  • LangGraph

  • AutoGen

  • Smolagents

  • Framework Selection: Trade-offs and considerations

08

Production Deployment

  • Production Architecture

  • Monitoring & Cost Control

  • Security Considerations

Final Project

AI Shopping Assistant

Design and build a conversational agent capable of handling both sales and customer support flows, including semantic and structured product search, cart management, agent routing, and human escalation, all integrated via tool use and memory.

Enroll now

TIME COMMITMENT

Specialization schedule

Specialization takes place on Tuesdays and Thursdays.

Enroll now

TIME COMMITMENT

Specialization schedule

Specialization takes place on Tuesdays and Thursdays.

Enroll now

FIND OUT

FIND OUT

FIND OUT

How it works

How it works

How it works

Live sessions
with experts

Live sessions with experts

Live sessions with experts

You will be able to get guidance from experienced instructors during live interactive sessions.

You will be able to get guidance from experienced instructors during live interactive sessions.

Flexible
Schedule

Flexible Schedule

Flexible Schedule

The program schedule is 4-week duration with 8 live sessions (32 hours total). Two 2-hour sessions per week.

The program schedule is 4-week duration with 8 live sessions (32 hours total). Two 2-hour sessions per week.

Eligibility
criteria

Eligibility criteria

Eligibility criteria

You must be a Graduated Fellow at Anyone AI and fully up to date with all contractual commitments.

You must be a Graduated Fellow at Anyone AI and fully up to date with all contractual commitments.

Get hands-on experience

Get hands-on experience

Get hands-on experience

You will build skills through hands-on learning with world-class instructors' support.

You will build skills through hands-on learning with world-class instructors' support.

TIME COMMITMENT

TIME COMMITMENT

Specialization schedule

Specialization schedule

Specialization takes place on Tuesdays and Thursdays.

Enroll now

Enroll now

LEARN WITH EXPERTS

LEARN WITH EXPERTS

LEARN WITH EXPERTS

About our instructor

About our instructor

About our instructor

SPECIALIZATION INSTRUCTOR

SPECIALIZATION INSTRUCTOR

View profile

Background

Background

Background

Msc. in Data Science from Universidad de Buenos Aires. AI Engineer with 5+ years of experience in Data Science, Machine Learning, LLMs, NLP, and multi-agent architectures, specializing in autonomous AI systems, RAG pipelines.

Expertise

Expertise

Expertise

He focuses on LLMs, NLP, and autonomous agent systems. He has led end-to-end AI implementations, designing multi-agent architectures, scalable LLM-powered applications, and RAG pipelines for document processing and automation. Beyond industry work, he has contributed to academic research and teaching Natural Language Processing (NLP) at leading universities.

NEVER STOP LEARNING

NEVER STOP LEARNING

The time is now — enroll today!

The time is now — enroll today!

NEVER STOP LEARNING

The time is now — enroll today!

NEVER STOP LEARNING

The time is now — enroll today!