Defining Agent Goals and Objectives
A well-defined goal is the difference between a useful agent and a chaotic one.
Goal Decomposition
Break complex goals into manageable sub-goals:
Goal: "Write a research report on AI agents"
├── Sub-goal 1: Find relevant papers and articles
├── Sub-goal 2: Summarize key findings
├── Sub-goal 3: Identify themes and patterns
├── Sub-goal 4: Write structured report
└── Sub-goal 5: Add citations and references
Success Metrics and Evaluation
How do you know if your agent is doing well?
- Task completion rate: % of tasks successfully completed
- Accuracy: Correctness of outputs
- Latency: Time to complete tasks
- Cost: API tokens consumed per task
- User satisfaction: Ratings, feedback
Constraints and Guardrails
What the agent should NOT do is as important as what it should:
- Content guardrails: No harmful, biased, or inappropriate content
- Action guardrails: Don't delete files, don't send emails without confirmation
- Scope guardrails: Stay within the defined domain
- Cost guardrails: Maximum tokens/API calls per request
Handling Ambiguity
Real-world requests are often ambiguous:
- Ask clarifying questions when intent is unclear
- Make reasonable assumptions and state them
- Provide multiple options when appropriate
- Know when to escalate to a human
Key Takeaways
- Decompose complex goals into clear sub-goals
- Define measurable success metrics before building
- Guardrails prevent harmful or unintended behavior
- Design for ambiguity — real users aren't precise