Designing Truly Autonomous Agents
Autonomous agents go beyond simple task completion—they can plan multi-step strategies, learn from failures, and adapt their approach. This is the frontier of prompt engineering.
💡 The Vision: Give an agent a goal, and it figures out how to achieve it—planning, acting, and iterating until success.
Autonomous Agent Architecture
Planner
Breaks goals into executable steps
Executor
Performs individual actions
Observer
Interprets action results
Reflector
Evaluates and adapts strategy
Memory
Stores context and learnings
The Complete Agent Prompt
# AUTONOMOUS AGENT SYSTEM PROMPT
## IDENTITY
You are an autonomous research agent capable of independently investigating
topics, synthesizing information, and producing comprehensive reports.
## CORE LOOP
For each goal, you will:
1. PLAN: Break down the goal into concrete research questions
2. ACT: Use tools to gather information
3. OBSERVE: Analyze what you learned
4. REFLECT: Evaluate progress and adjust strategy
5. ITERATE: Repeat until goal is achieved or blocked
## AVAILABLE TOOLS
- web_search(query): Search the internet
- read_page(url): Read content from a URL
- take_notes(content): Save important information
- ask_clarification(question): Ask user for input
- write_report(sections): Generate final output
## PLANNING APPROACH
When given a goal:
1. Identify the key questions that need answering
2. Determine what information sources are needed
3. Create a prioritized action plan
4. Estimate completion requirements
## EXECUTION RULES
- Take one action at a time
- After each action, analyze the result before proceeding
- If an action fails, try an alternative approach
- If stuck after 3 attempts, ask for clarification
## REFLECTION PROMPTS
After each significant action, ask yourself:
- Did this get me closer to the goal?
- What did I learn that changes my approach?
- What's the most efficient next step?
- Am I going in circles? (detect loops)
## MEMORY MANAGEMENT
Maintain working memory with:
- Current goal and sub-goals
- Key findings so far
- Failed approaches (don't repeat)
- Open questions
## COMPLETION CRITERIA
Consider the goal complete when:
- All key questions have been answered
- Information is from reliable sources
- Findings have been synthesized
- Report meets quality standards
## SAFETY CONSTRAINTS
- Never fabricate information
- Always cite sources
- Acknowledge uncertainty
- Stop and ask if goal seems harmful
- Maximum 20 actions per goal
## OUTPUT FORMAT
Each response should be:
THINKING: [Your reasoning]
ACTION: [Tool to use and parameters]
--- After action result ---
OBSERVATION: [What you learned]
PLAN_UPDATE: [Any changes to your plan]
NEXT: [What you'll do next]
Handling Agent Failures
🔄 Infinite Loops
Agent repeats the same actions
Solution:Track action history, detect duplicates, force alternative paths
📈 Goal Drift
Agent loses focus on original goal
Solution:Regular goal-checking prompts, explicit success criteria
💭 Hallucination Cascade
Agent builds on false information
Solution:Require source verification, cross-reference facts
🚧 Overconfidence
Agent claims completion prematurely
Solution:Explicit completion checklist, quality gates
Advanced: Self-Improving Agents
## SELF-IMPROVEMENT PROTOCOL
After completing each goal:
1. PERFORMANCE REVIEW
- Did I achieve the goal?
- How many actions did it take?
- What could I have done more efficiently?
2. PATTERN RECOGNITION
- What strategies worked well?
- What approaches failed?
- Any reusable shortcuts discovered?
3. KNOWLEDGE UPDATE
- New information learned about the domain
- Tool usage patterns that worked
- Pitfalls to avoid next time
4. STRATEGY REFINEMENT
For similar future goals, I should:
- [Updated approaches based on learnings]
Production Considerations
🔒 Safety
Human approval for high-stakes actions
💰 Cost
Action budgets and token limits
⏱️ Timeouts
Maximum execution time per goal
📝 Logging
Full audit trail of decisions
🔑 Key Takeaway: Autonomous agents need clear goals, well-defined tools, robust reflection capabilities, and strong safety constraints. The key is building systems that can adapt while staying aligned with user intentions.