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An Introduction to Agentic and Generative AI
Over the past two years, generative AI has driven nearly every conversation in technology.
Companies have raced to integrate tools like ChatGPT, Claude, Gemini, and Copilot into workflows across engineering, support, marketing, and product development.
Teams have experimented aggressively, run pilots, and built proofs of concept. Some companies achieved meaningful productivity gains, while others struggled to move beyond one-off use cases.
Now, a new category is gaining attention: agentic AI.
If generative AI reshaped how people interact with information, agentic AI is reshaping how software behaves. The jump from “generate text” to “take actions” is the shift from a predictive assistant to an autonomous system.
Industry analysts, research labs, and leading vendors have all moved in this direction. Meta, OpenAI, Google, Anthropic, and a wave of startups are releasing agent frameworks and agent orchestration platforms. New enterprise platform vendors are positioning agent systems as the next layer of automation.
This shift is not hype. It reflects a deeper evolution in AI system design. Generative AI unlocked a new human interface. Agentic AI is unlocking a new machine interface.
In this week’s newsletter, we will break down the differences between agentic AI and generative AI, why this evolution matters, and what you should know for 2026.
What is Agentic AI?
Agentic AI refers to AI systems that can take actions, pursue goals, and operate autonomously in digital or real-world environments. Generative AI focuses on producing content. Agentic AI focuses on completing tasks.
The term “agent” has existed in AI research for decades. Reinforcement learning, robotic, and rule-based agents have been part of classical AI. What changed is that large language models created a flexible reasoning engine that can power general-purpose agents without the heavy manual engineering required in the past.
Modern agentic AI systems combine three core capabilities:
Planning: The system can break down a goal into steps. For example, “create an onboarding workflow in HubSpot” becomes a series of interactions with tools.
Action taking: The system can execute commands, navigate interfaces, call APIs, write code, or use software tools.
Reflection and feedback loops: Agents monitor results, detect errors, and adjust their approach. They retry, refine, and update plans.
In short, agentic AI moves AI from answering questions to completing objectives.
Agentic AI is getting mainstream attention now for three reasons:
Enterprise teams want automation that goes beyond chat: Executives are asking for measurable efficiency gains, not just enhanced communication.
Tool use is the next frontier: Vendors have added function-calling, retrieval, and structured reasoning, making agents much more reliable.
Engineering teams are building AI into products: Companies are shifting from “use an AI tool” to “our software has an AI system that takes actions for users.”
Agentic systems will not replace generative AI. They build on it. Generative AI provides the reasoning layer. Agentic AI provides the execution layer.
What is Generative AI?
Generative AI produces new content based on existing data. That content can be text, images, code, audio, or video. The core idea is prediction. Models predict the next token in a sequence or the structure of an image based on learned patterns.
Popular examples include:
Large language models like GPT-4 and Claude 3: Used for summarization, drafting, analysis, writing, tutoring, and many other tasks.
Image models like Midjourney, OpenAI’s Sora, and Stable Diffusion: Used in design, media, entertainment, and creative workflows.
Code generation models: Tools like GitHub Copilot, Amazon CodeWhisperer, and Replit’s code models have become mainstream for engineering teams.
Why did generative AI become the fastest adopted technology in history?
Generative AI succeeded for one simple reason. It provides immediate value with zero configuration. People type in plain language, get a useful output, and iterate. This speed of adoption changed workflows across engineering, support, design, and operations.
But generative AI has limitations:
It’s reactive: it waits for prompts.
It does not execute tasks end-to-end: it produces content but does not take action unless paired with tools.
It often lacks long-term memory or goals: it requires consistent human intervention or assistance.
These limitations created the space for agentic AI to evolve. Companies realized that content generation alone does not transform a workflow. Execution does.
Key Features of Agentic AI and Generative AI
Below is a breakdown of the major features of each and why they matter.
Key features of agentic AI:
Goal-oriented behavior: Agents optimize toward an outcome. For example, “file this expense report and notify finance.”
Multi-step reasoning: Agents can chain steps together and adjust when steps fail.
Tool and API integration: Modern agent frameworks allow systems to call functions, interact with databases, send emails, execute scripts, or run workflows.
Memory and context management: Agents use long-term memory stores to retain state, history, and knowledge.
Autonomy within constraints: Agents run inside safety boundaries. They can explore and solve problems while respecting guardrails.
Collaboration with other agents: Multi-agent systems divide complex tasks across roles, similar to microservices.
Key features of generative AI:
Content creation: Text, code, audio, video, images. Predictive content generation at scale.
Natural language interface: Humans describe what they want. The model generates a response.
Fine-tuning and domain adaptation: Teams can adapt models to industry-specific content.
Embeddings and retrieval: Retrieval-augmented generation (RAG) improves accuracy by giving models access to up-to-date information.
Flexible reasoning: Generative models can reason about problems, rewrite content, debug code, and analyze documents.
Agentic AI vs Generative AI Comparison
Below is a comparison for planning AI strategies, highlighting key differences between Agentic AI and Generative AI:
Criteria | Agentic AI | Generative AI |
|---|---|---|
System purpose | Task completion | Content creation |
Output type | Outcomes and actions | Text or media |
Operation mode | Proactive | Reactive |
Multi-step reasoning | Strong | Limited |
Memory | Persistent | Short lived |
Integrations | Extensive | Minimal |
Engineering complexity | High | Low to moderate |
Primary risks | Incorrect actions | Incorrect content |
Governance | Strong | Light |
Best use cases | Automation, operations | Writing, coding |
Business value | Organizational productivity | Individual productivity |
1. Purpose of the System:
Generative AI: Produces new content such as text, code, images, audio, or video.
Agentic AI: Achieves a defined goal by taking actions across tools, systems, or environments.
2. Primary Output:
Generative AI: Content output.
Agentic AI: Completed tasks, workflows, or state changes in a system.
3. Mode of Operation:
Generative AI: Reactive. Waits for explicit user prompts.
Agentic AI: Proactive. Initiates actions based on goals, events, or triggers.
4. Core Capabilities:
Generative AI:
Text and code generation
Summarization
Analysis and reasoning
Pattern prediction
Agentic AI:
Planning
Tool execution
Multi step workflows
Error recovery and retries
State management
5. Decision-Making Model:
Generative AI: Short form reasoning within a single output window.
Agentic AI: Extended reasoning across multiple steps with reflection loops.
6. Memory and State:
Generative AI: Limited contextual memory; no persistent state unless externally managed.
Agentic AI: Persistent memory, long term task context, and state tracking.
7. Integration Requirements:
Generative AI: Can operate standalone with minimal integration.
Agentic AI: Requires system integrations such as APIs, event streams, tools, and permission layers.
8. Skill Requirements for Engineering Teams:
Generative AI:
Prompt design
RAG and embeddings
Model selection and evaluation
Code generation workflows
Agentic AI:
Orchestration design
Tool schema creation
Guardrails and safety gates
Observability and monitoring
Workflow automation engineering
9. Error Types:
Generative AI:
Hallucinations
Misinterpretation of prompts
Incorrect or incomplete content
Agentic AI:
Incorrect actions
Mis-sequenced steps
Permissions failures
Irrecoverable state changes
System-level side effects
10. Risk Surface
Generative AI: Low to moderate. Mostly reputational or quality risks.
Agentic AI: Higher risk. Actions can affect systems of record, financial data, security boundaries, or customer workflows.
11. Observability Required
Generative AI: Output inspection and lightweight evaluation.
Agentic AI:
Step-level logs
Tool call tracking
Action approvals or checkpoints
Long-running task monitors
12. Governance Requirements
Generative AI: Data governance, accuracy testing, content controls.
Agentic AI:
Access control
Role-based permissions
Audit trails
Compliance verification
Safe execution environments
13. Best Use Cases
Generative AI:
Text drafting
Code suggestions
Knowledge retrieval
Data insights
Image and media creation
Agentic AI:
CRM workflow automation
IT and security operations
Financial reconciliation
Customer support actions
HR onboarding flows
Multi system business processes
14. Dependencies
Generative AI: Often a single model or API endpoint.
Agentic AI:
Multiple tools
Orchestration engines
Policy layers
Memory stores
Event triggers
15. Time Horizon for Value
Generative AI: Immediate productivity gains for individuals.
Agentic AI: Medium to long-term gains across teams and operations.
This is also why companies like McKinsey, Gartner, and BCG are predicting that the next phase of AI value creation will come from agent workflows rather than standalone generative use cases.
16. Primary Value to the Business
Generative AI: Efficiency for knowledge workers.
Agentic AI: Automation at scale for end-to-end processes.
How To Learn Agentic and Generative AI
Engineering and enablement leaders are now asking the same question: how do we upskill teams on agentic AI and generative AI?
Below is a practical roadmap for organizations, based on trends from industry training programs, vendor roadmaps, and internal enablement efforts.
1. Start with generative AI fundamentals
Every engineer needs working knowledge of:
Prompting patterns
RAG systems
Embeddings
Model limitations
Code generation
Reasoning and analysis workflows
Safety and data handling
Even companies investing heavily in agents start here because agents rely on strong generative reasoning.
2. Learn structured reasoning and tool use
This is the bridge between generative and agentic systems. Modern models support:
Function calling
Tool invocation
JSON mode and structured outputs
Workflow orchestration
Teams need to be able to design tools, define schemas, and connect models to existing systems.
3. Study agent frameworks and orchestration platforms
Frameworks to explore include:
Microsoft AutoGen
LangChain Agents
OpenAI’s Operator
Google’s Workspace Agents
Anthropic tool use examples
CrewAI and Agentverse
React-style multi-step reasoning patterns
Understanding these frameworks is critical for designing reliable agent behavior.
4. Learn to build guardrails and governance
Agent systems need:
Access control
Audit logs
Allow and block lists
Rate limiting
Safety policies
Evaluation metrics
Monitoring and observability
Generative AI had failure modes that were often harmless. Agentic systems do not have that luxury.
5. Practice building real agent workflows
Some accessible starting points:
Create an agent that files Jira tickets from Slack
Build a code review agent
Build an agent that updates a CRM
Build an HR onboarding agent
Build a finance reconciliation agent
Small projects teach teams how to design, deploy, and monitor agent behavior.
6. Build organizational agent literacy
Most companies fail at AI not because the models are weak but because teams misunderstand how to use them. Effective organizations run internal training programs for:
Engineering teams
Product managers
Analysts
Designers
Operations teams
Every role needs to understand how agent systems work and where to apply them.
7. Prioritize hands-on practice rather than passive learning
The most effective companies combine:
Real project builds
Pair programming with AI
Codebase navigation exercises
Internal hackathons
Weekly prompt jams
Guided agent development
This is the difference between knowing about AI and being able to build with AI.
Agentic AI vs Generative AI: Top 10 Frequently Asked Questions
Agentic AI vs generative AI is now one of the most important comparisons in enterprise technology.
Generative AI focuses on content generation. Agentic AI focuses on autonomous task completion. Understanding these differences helps organizations select the right AI approach for automation, productivity, and system integration.
1. What is the main difference between agentic AI and generative AI?
The core difference is that generative AI creates content while agentic AI completes tasks. Generative AI outputs text, code, or media. Agentic AI uses planning, tools, and actions to achieve a goal.
2. What is agentic AI?
Agentic AI is a type of artificial intelligence that can plan, execute actions, use tools, and complete multi-step workflows. It operates autonomously within digital systems, making it suitable for business process automation.
3. What is generative AI?
Generative AI produces new content based on learned patterns. This includes text generation, code generation, image creation, and document summarization.
4. How does agentic AI work?
Agentic AI works by combining:
Goal setting
Planning and reasoning
Tool or API execution
Memory and state tracking
Error detection and retries
This allows the agent to complete tasks end-to-end.
5. How does generative AI work?
Generative AI predicts and generates content using large language models or multimodal models. It responds to user prompts with text, code, images, or structured outputs.
6. What are the best use cases for agentic AI?
Agentic AI is best for workflow automation, including:
CRM updates
HR onboarding processes
Finance and accounting tasks
IT operations automation
Customer support actions
These use cases require actions, not just content.
7. What are the best use cases for generative AI?
Generative AI is ideal for content focused tasks, such as:
Drafting email
Writing code
Summarizing documents
Analyzing text
Creating images or visuals
8. What risks come with agentic AI compared to generative AI?
Generative AI risks: Inaccurate content, hallucinations, misinterpretation.
Agentic AI risks: Incorrect actions, permission issues, changes to systems of record, compliance problems.
Agentic AI requires stronger safety and governance controls.
9. Do agentic AI systems use generative AI models?
Yes. Most agentic AI systems depend on generative AI models for reasoning, decision-making, and tool selection. Generative AI is often the foundation that powers agentic behavior.
10. How should organizations choose between agentic AI and generative AI?
Choose generative AI when the goal is to generate or transform content.
Choose agentic AI when the goal is to automate workflows and complete tasks.
The decision depends on whether you need content output or action output.
Agentic AI vs Generative AI Is The Next Major Divide In Enterprise Technology
Generative AI changed how people work. Agentic AI will change how software works.
Companies that understand this shift early will build the next category of products and dramatically increase the efficiency of their teams. Companies that wait will fall behind.
Both require new skills. Both require new engineering patterns. And both require structured training programs to bring teams up to speed.
If generative AI was the first wave of transformation, agentic AI is the multiplier. It is the moment where AI moves from assisting humans to acting as a collaborator embedded directly inside systems.
As you plan your 2026 and 2027 technology roadmaps, the question is no longer whether you should adopt AI. The question is whether your organization is ready for the agent era.
If you’re ready to accelerate your AI transformation, request a demo to learn how TryTami can help:
Until next Tuesday,
Kelby, Dean, & Dave
