6 MIN READ
The 4 Levels of AI Delegation: From Assistant to Autonomous Agent
By Learnia Team
This article is written in English. Our training modules are available in French.
How much control should you give AI? The answer isn't binary. There's a spectrum from "AI as a tool" to "AI as an autonomous agent." Understanding these levels helps you choose the right approach for each task.
The 4 Delegation Levels
| Level | Name | AI Does | Human Does | |-------|------|---------|------------| | 1 | Generation | Generates text/content | Everything else | | 2 | Retrieval (RAG) | Searches and synthesizes | Validates, decides | | 3 | Tool Use (ReAct) | Calls functions/APIs | Approves, monitors | | 4 | Autonomous Agent | Plans and executes | Defines goals, oversees |
Each level increases AI autonomy and risk.
Level 1: Pure Generation
What It Does
AI generates text based on its training:
- Answers questions from memory
- Creates content
- Explains concepts
Example
User: "Write a product description for noise-canceling headphones"
AI: [Generates description based on training knowledge]
No external data, no actions—just generation.
Characteristics
✅ Simple, fast, predictable
✅ No external dependencies
✅ Easy to understand and audit
❌ Limited to training knowledge
❌ Can't access current information
❌ May hallucinate facts
Best For
- Creative writing
- Explanations of stable concepts
- Brainstorming
- Quick drafts
Level 2: Retrieval (RAG)
What It Does
AI searches your documents, then generates:
- Finds relevant information
- Synthesizes from sources
- Cites references
Example
User: "What's our vacation policy for new employees?"
AI Process:
1. Search company documents
2. Find HR policy document
3. Extract relevant section
4. Generate answer with citation
Answer: "According to the HR Policy (v2.3), new employees
receive 15 days paid vacation after 3 months of employment."
Characteristics
✅ Grounded in your data
✅ More accurate for specific knowledge
✅ Can cite sources
❌ Still read-only
❌ Can't take actions
❌ Quality depends on retrieval
Best For
- Internal knowledge bases
- Customer support
- Document Q&A
- Research assistance
Level 3: Tool Use (ReAct)
What It Does
AI can call external functions:
- Execute calculations
- Query APIs
- Access real-time data
- Perform limited actions
Example
User: "What's 15% of our Q3 revenue? And send the result to finance."
AI Process:
1. Think: Need revenue data and calculation
2. Action: query_database("Q3 revenue")
3. Observation: $2.4M
4. Action: calculate(2400000 * 0.15)
5. Observation: $360,000
6. Action: send_email(to="finance", subject="Q3 15%", body="...")
7. Response: "15% of Q3 revenue is $360,000. Email sent to finance."
Characteristics
✅ Can take real actions
✅ Access to real-time data
✅ Accurate calculations
⚠️ Actions have consequences
⚠️ Needs careful permission design
⚠️ Requires monitoring
Best For
- Data analysis with calculations
- Information lookup + action
- Workflow automation (with oversight)
- Integration with business systems
Level 4: Autonomous Agent
What It Does
AI plans and executes multi-step goals:
- Breaks down complex objectives
- Decides on approach
- Executes without step-by-step approval
- Iterates until goal achieved
Example
User: "Research competitors and prepare a market analysis report"
AI Autonomous Process:
1. Plan: Identify competitors, gather data, analyze, report
2. Search web for competitor information
3. Query internal sales data
4. Compare pricing, features, positioning
5. Generate analysis document
6. Create visualizations
7. Compile final report
8. Save to shared drive
9. Notify stakeholders
Human involvement: Goal definition and final review only
Characteristics
✅ Handles complex, multi-step tasks
✅ Minimal human intervention
✅ Can work on long-running goals
⚠️ Higher risk if goes wrong
⚠️ Harder to predict behavior
⚠️ Needs strong guardrails
⚠️ Requires trust in the system
Best For
- Research and analysis projects
- Complex workflows with clear goals
- Tasks where efficiency matters more than control
- Well-defined, repeatable processes
Choosing the Right Level
The Decision Framework
How critical is accuracy?
├── Very critical → Level 1-2 (more human oversight)
└── Moderate → Level 3-4 possible
Are real-world actions needed?
├── No → Level 1-2
└── Yes → Level 3-4
How complex is the task?
├── Single-step → Level 1-3
└── Multi-step planning → Level 4
What's the cost of errors?
├── High (legal, financial) → Lower levels + oversight
└── Low (internal draft) → Higher levels acceptable
Risk vs Efficiency Trade-off
Level 1: Low risk, low efficiency, high control
Level 2: Low risk, medium efficiency, high control
Level 3: Medium risk, high efficiency, medium control
Level 4: Higher risk, highest efficiency, lower control
Mixing Levels
Real systems often combine levels:
Example: Customer Support Bot
Level 2 (RAG): Answer product questions from docs
Level 3 (Tools): Check order status via API
Level 1 (Generation): Craft personalized response
Different levels for different parts of the task.
Example: Research Assistant
Level 4 (Autonomous): Gather and organize information
Level 1 (Generation): Create initial draft
Level 2 (RAG): Fact-check against sources
Human: Final review and approval
Autonomous for gathering, controlled for output.
Guardrails by Level
Level 1-2: Lighter Controls
- Output filtering
- Hallucination detection
- Response review for sensitive topics
Level 3: Medium Controls
- Confirmation for destructive actions
- Rate limiting
- Permission scoping
- Audit logging
Level 4: Stronger Controls
- Goal boundaries (what it can't do)
- Budget limits (time, API calls, cost)
- Mandatory checkpoints
- Human escalation triggers
- Rollback capabilities
The Future: Level 5?
Emerging: Multi-Agent Collaboration
Multiple AI agents working together:
- Research agent gathers data
- Analysis agent interprets
- Writing agent creates report
- Review agent checks quality
- Coordinator agent manages workflow
Even more autonomous, even more powerful, even more risk.
This is active research territory—exciting but requires careful development.
Key Takeaways
- →4 levels: Generation → RAG → Tool Use → Autonomous
- →Each level increases autonomy and risk
- →Choose based on task complexity, accuracy needs, error cost
- →Mix levels within a single system
- →Guardrails scale with autonomy level
Ready to Build AI Agents?
This article covered the what and why of AI delegation levels. But implementing agents safely requires deep understanding of architecture and controls.
In our Module 6 — AI Agents & ReAct, you'll learn:
- →ReAct framework implementation
- →Tool design and permission systems
- →Multi-step agent architectures
- →Safety guardrails and monitoring
- →From Level 3 to Level 4 patterns
GO DEEPER
Module 6 — AI Agents & ReAct
Create autonomous agents that reason and take actions.