Prompt Structure Components

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1. Role definition

• Define the agent’s identity and domain expertise (for example, “You are a Kubernetes expert with deep knowledge of cluster operations”).
• State experience level and scope of authority to set user expectations and constrain behavior.

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2. Goal statement

• Clearly state the agent’s primary objective (for example, “Your goal is to help users manage their Kubernetes cluster”).
• A concise goal guides decision-making and keeps outputs aligned with user intent.

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3. Operational protocols

• Describe how the agent begins work (initial assessment), the execution strategy (step-by-step process), and troubleshooting flows.
• Include validation rules: how the agent verifies actions, when it asks clarifying questions, and when it escalates or stops.
• Define expected step boundaries (what to attempt and what to avoid).

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4. Safety guidelines

• Specify risk-management constraints (for example: minimize disruption, operate in read-only mode by default, favor small incremental changes).
• Enumerate forbidden actions and contextual constraints (e.g., do not delete resources without explicit confirmation).

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5. Tool descriptions

• For each tool the agent can access, define:
  • Purpose and typical use cases.
  • Inputs and outputs (data formats and example payloads).
  • Interaction patterns with other tools.
  • Example usage snippets.
• Ensure tools are unambiguous and mutually compatible to avoid overlap.

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6. Best practices

• Start simple: define a basic role and purpose first, then add tools and complexity.
• Avoid assigning multiple overlapping tools to the same agent to reduce ambiguity.
• Be explicit—spell out behaviors and constraints; do not rely on implicit assumptions.

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7. Behavior guidelines

• Provide concrete rules for runtime behavior, such as:
  • “Always confirm destructive changes before applying.”
  • “Log each action and its rationale.”
  • “Ask for missing information or clarification.”
• Use examples to illustrate allowed vs. disallowed behaviors.

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8. Structure for clarity

• Use numbered sections, bullet lists, and short paragraphs so models can parse instructions reliably.
• Explicit, structured prompts produce more predictable behavior from LLMs.

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9. Iterate and redefine

• Treat agent definition as iterative:
  • Create an initial prompt, test with real queries, analyze outputs, and refine.
  • Schedule regular reviews and A/B tests to detect regressions or drift.

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10. Advanced technique: use another model to improve prompts

• Use a second LLM (for example, ChatGPT or Claude) to:
  • Review and improve your system prompt.
  • Suggest clearer role statements and tool descriptions.
  • Propose alternative phrasing or edge cases to handle.
• Always validate suggestions—models can hallucinate plausible-sounding but incorrect recommendations.

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Share common prompts across agents

• Inline in the agent definition — fast for experiments.
• ConfigMap — reusable and non-sensitive prompts.
• Secret — for sensitive prompts (store base64-encoded values).

apiVersion: v1
kind: Secret
metadata:
  name: my-agent-secret
  namespace: kagent
data:
  # Replace the value with the base64-encoded prompt, for example: echo -n "your-prompt" | base64
  system-message: `base64-encoded-prompt`

# Example: reference a prompt stored in a ConfigMap
declarative:
  systemMessageFrom:
    type: ConfigMap
    name: my-agent-config
    key: system-message

# Example: reference a prompt stored in a Secret
declarative:
  systemMessageFrom:
    type: Secret
    name: my-agent-secret
    key: system-message

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Checklist for prompt content

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Summary: prompt evolution

• Start with a minimal working prompt: role + purpose.
• Add tools and quick usage notes.
• Expand per-tool descriptions, examples, and operational protocols.

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Summary of key principles

• Start simple and add complexity gradually.
• Be explicit about desired behavior and constraints.
• Structure prompts for clarity (numbered lists and sections).
• Iterate and refine from real-world testing and trace analysis.

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Storage options recap

• Inline in the agent definition — ideal for quick experiments.
• ConfigMap — reusable, non-sensitive prompts for teams.
• Secret — store sensitive prompts base64-encoded.

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Next steps

• Upcoming materials will cover troubleshooting agents using traces and logs.
• Hands-on exercises will show how to build both declarative and imperative agents so you can apply these concepts in practice.