Agentic AI
Agentic AI refers to AI systems that can autonomously make decisions and take actions.
Classification
- ComplexityMedium
- Impact areaTechnical
- Decision typeDesign
- Organizational maturityIntermediate
Technical context
Principles & goals
Use cases & scenarios
Compromises
- Misuse of Technology
- Unforeseen Decisions
- Loss of Human Control
- Regular Review of Decisions
- User Training
- Transparent Communication
I/O & resources
- Data Sources
- User Inputs
- Environmental Information
- Decisions
- Actions
- Reports
Description
Agentic AI is a concept that deals with the development of artificial intelligence capable of making autonomous decisions and taking actions. These systems are designed to operate in complex environments and adapt to changing conditions, enabling a wide range of applications across various fields.
✔Benefits
- Increased Efficiency
- Improved Decision Quality
- Reduced Human Errors
✖Limitations
- Dependence on Data Quality
- Ethical Concerns
- Technological Complexity
Trade-offs
Metrics
- Decision Accuracy
Measuring the accuracy of decisions made by the Agentic AI.
- Response Time
Time taken to respond to inquiries or situations.
- User Satisfaction
Evaluating user satisfaction with the decisions made by the Agentic AI.
Examples & implementations
Tesla Autopilot
An example of Agentic AI that enables autonomous driving in various traffic situations.
Apple's Siri
An intelligent personal assistant that understands natural language and responds to user queries.
Chatbots in Customer Service
Automated systems that respond to customer inquiries in real-time.
Implementation steps
Identify Data Sources
Build Technological Infrastructure
Develop Ethical Guidelines
⚠️ Technical debt & bottlenecks
Technical debt
- Outdated Technologies
- Insufficient Documentation
- Lack of Maintenance
Known bottlenecks
Misuse examples
- Use of Biased Data
- Abuse of Decision Autonomy
- Insufficient Oversight of AI Decisions
Typical traps
- Assuming AI is Always Correct
- Neglecting Human Intuition
- Overlooking Context Factors
Required skills
Architectural drivers
Constraints
- • Compliance with Data Protection Regulations
- • Technical Infrastructure
- • Resource Allocation