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Agentic Workflows

Learn how to design, orchestrate, and deploy agentic AI workflows using large language models, tools, memory, and reasoning loops to build autonomous,
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As artificial intelligence systems evolve beyond simple question-answering and text generation, a new paradigm has emerged: agentic AI workflows. Instead of responding passively to a single prompt, agentic systems are designed to reason, plan, act, observe, and adapt across multiple steps to achieve complex goals. These systems behave more like autonomous problem solvers than traditional AI models, making them suitable for real-world applications that require decision-making, tool usage, and iterative reasoning.
 
Agentic workflows represent a major shift in how AI applications are built. Rather than relying on static pipelines, developers now design AI agents that can decompose tasks, choose tools, maintain memory, interact with external systems, and refine their behavior based on feedback. This approach powers modern AI assistants, autonomous research agents, coding copilots, workflow automation tools, customer support bots, and decision-support systems.
 
The Agentic Workflows course by Uplatz provides a comprehensive and practical guide to designing, implementing, and deploying agent-based AI systems using large language models (LLMs). You will learn how to move from single-prompt applications to multi-agent, multi-step workflows that operate reliably in production environments. The course focuses on both conceptual foundations and hands-on engineering patterns used in modern agentic frameworks.
🔍 What Are Agentic Workflows?
 
Agentic workflows are AI systems in which one or more agents:

  • Understand goals

  • Plan actions

  • Execute tasks using tools

  • Observe results

  • Adjust behavior based on outcomes

An agent typically consists of:

  • A language model (LLM)

  • A reasoning loop (plan → act → observe)

  • Access to tools (APIs, databases, code, search)

  • Memory (short-term and long-term)

  • Control logic (rules, constraints, safety checks)

Agentic workflows go beyond simple prompt-response models and enable AI systems to operate autonomously over time.
⚙️ How Agentic Workflows Work
 
Agentic systems are built around iterative reasoning and execution cycles.
 
1. Goal Definition
 
The system receives a goal such as:

  • “Research this topic and summarize findings”

  • “Fix this bug and propose a solution”

  • “Plan a marketing campaign”

2. Planning & Decomposition
 
The agent breaks the goal into smaller sub-tasks:

  • Gather information

  • Analyze data

  • Generate outputs

  • Validate results

3. Tool Use
 
Agents call external tools such as:

  • Web search APIs

  • Databases

  • Code execution environments

  • Vector databases

  • Enterprise systems

4. Memory Management
 
Agents maintain:

  • Short-term memory for current context

  • Long-term memory for past interactions, documents, or learned preferences

5. Observation & Reflection
 
After executing actions, the agent evaluates:

  • Did the action succeed?

  • Is more information needed?

  • Should the plan change?

6. Iteration & Completion
 
The agent loops until the goal is achieved or termination criteria are met.
 
These steps form the backbone of agentic workflows used in modern AI products.
🏭 Where Agentic Workflows Are Used in Industry
 
Agentic systems are rapidly being adopted across sectors:
 
1. Software Engineering
 
Autonomous coding assistants, debugging agents, test generation, and code review bots.
 
2. Research & Knowledge Work
 
AI research agents that read papers, summarize findings, and generate reports.
 
3. Customer Support & Operations
 
Multi-step resolution agents that retrieve data, take actions, and follow up with users.
 
4. Business Process Automation
 
Agents that automate workflows across CRM, ERP, HR, and finance systems.
 
5. Data Analysis & BI
 
Agents that query databases, generate insights, and create dashboards.
 
6. Marketing & Content Creation
 
Campaign planning agents, content generation pipelines, SEO analysis agents.
 
7. Education & Training
 
Personalized tutors that adapt lessons, evaluate progress, and adjust strategies.
 
Agentic workflows enable AI systems to move from assistive to autonomous.
🌟 Benefits of Learning Agentic Workflows
 
By mastering agentic workflows, learners gain:

  • Ability to build autonomous AI systems

  • Skills in multi-step reasoning and planning

  • Practical understanding of tool-calling and memory

  • Knowledge of modern agent frameworks

  • Experience designing safe and controllable agents

  • High-value skills for GenAI and AI platform roles

Agentic workflows are becoming a core competency for modern AI engineers.
📘 What You’ll Learn in This Course
 
You will explore:

  • Foundations of agentic AI

  • Single-agent vs multi-agent systems

  • Planning, reasoning, and execution loops

  • Tool-calling patterns and APIs

  • Memory architectures (buffers, vector memory)

  • Agent orchestration and coordination

  • Error handling and fallback strategies

  • Evaluation of agent behavior

  • Safety, alignment, and control mechanisms

  • Real-world agentic use cases

  • Capstone: build an end-to-end agentic workflow

🧠 How to Use This Course Effectively

  • Start with simple single-agent workflows

  • Practice designing clear goals and constraints

  • Gradually add tools and memory

  • Experiment with different planning strategies

  • Evaluate agent outputs critically

  • Build and refine a complete agentic system as your capstone

👩‍💻 Who Should Take This Course
 
This course is ideal for:

  • Machine Learning Engineers

  • LLM Developers

  • AI Product Engineers

  • Backend Engineers building AI systems

  • Automation Engineers

  • Data Scientists

  • Students entering GenAI development

Basic Python and familiarity with LLMs is helpful.
🚀 Final Takeaway
 
Agentic workflows represent the future of applied AI — systems that can reason, plan, act, and adapt autonomously. By mastering agentic workflows, you gain the ability to design AI systems that go far beyond chat interfaces and become intelligent collaborators capable of executing real-world tasks.
 

Course Objectives Back to Top

By the end of this course, learners will:

  • Understand the principles of agentic AI

  • Design goal-driven AI workflows

  • Build agents with reasoning, tools, and memory

  • Implement single-agent and multi-agent systems

  • Orchestrate agents reliably in production

  • Apply safety and control strategies

  • Deploy agentic workflows for real applications

Course Syllabus Back to Top

Course Syllabus

Module 1: Introduction to Agentic AI

  • From prompts to agents

  • Why agentic workflows matter

Module 2: Agent Architecture

  • LLM core

  • Tools

  • Memory

  • Control logic

Module 3: Reasoning & Planning

  • Chain-of-Thought

  • Plan-Act-Observe loops

Module 4: Tool Calling & Integration

  • APIs

  • Databases

  • Code execution

Module 5: Memory Systems

  • Short-term memory

  • Vector databases

  • Long-term memory strategies

Module 6: Multi-Agent Systems

  • Collaboration

  • Delegation

  • Role-based agents

Module 7: Orchestration Patterns

  • Sequential workflows

  • Parallel agents

  • Event-driven systems

Module 8: Reliability & Safety

  • Error handling

  • Guardrails

  • Alignment

Module 9: Evaluation & Monitoring

  • Measuring agent performance

  • Feedback loops

Module 10: Capstone Project

  • Build a complete agentic workflow for a real-world use case

Certification Back to Top

Course Syllabus

Module 1: Introduction to Agentic AI

  • From prompts to agents

  • Why agentic workflows matter

Module 2: Agent Architecture

  • LLM core

  • Tools

  • Memory

  • Control logic

Module 3: Reasoning & Planning

  • Chain-of-Thought

  • Plan-Act-Observe loops

Module 4: Tool Calling & Integration

  • APIs

  • Databases

  • Code execution

Module 5: Memory Systems

  • Short-term memory

  • Vector databases

  • Long-term memory strategies

Module 6: Multi-Agent Systems

  • Collaboration

  • Delegation

  • Role-based agents

Module 7: Orchestration Patterns

  • Sequential workflows

  • Parallel agents

  • Event-driven systems

Module 8: Reliability & Safety

  • Error handling

  • Guardrails

  • Alignment

Module 9: Evaluation & Monitoring

  • Measuring agent performance

  • Feedback loops

Module 10: Capstone Project

  • Build a complete agentic workflow for a real-world use case

Career & Jobs Back to Top

This course prepares learners for roles such as:

  • LLM Engineer

  • AI Engineer (Agentic Systems)

  • GenAI Developer

  • Automation Engineer

  • AI Product Engineer

  • ML Platform Engineer

Interview Questions Back to Top

1. What is an agentic workflow?

A multi-step AI system that plans, acts, observes, and adapts to achieve a goal.

2. How is an agent different from a chatbot?

Agents can use tools, maintain memory, and execute tasks autonomously.

3. What components make up an AI agent?

LLM, tools, memory, reasoning loop, and control logic.

4. What is tool calling?

Allowing an agent to invoke external APIs or systems during execution.

5. What is agent memory?

Stored context or knowledge that persists across steps or sessions.

6. What is a multi-agent system?

Multiple agents collaborating or delegating tasks.

7. Why are agentic workflows powerful?

They enable autonomy, adaptability, and complex task execution.

8. What are common risks in agentic systems?

Hallucinations, infinite loops, unsafe actions.

9. How do you control agent behavior?

Using constraints, guardrails, monitoring, and evaluation.

10. Where are agentic workflows used today?

Autonomous research, coding agents, workflow automation, enterprise AI systems.

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