Tool Execution Flow model decides to call a tool tool executes structured output is returned AI uses the result in its response Common Errors missing required fields unclear parameters invalid inputs Best Practices for Errors prevent missing fields include validation return structured errors avoid vague messages Do’s be specific about missing fields explain why it failed provide clear status Don’ts vague messages no explanation no guidance
Context Engineering Context Engineering is about selecting and managing the right information for the AI system to produce its best performance. It is the extension of prompt engineering. Prompt vs Context Prompt narrow static Context broader approach what to include where to pass info how to keep context manageable Why Context Matters context shapes AI agent behaviour context determines output quality context enables better decisions What Context Includes system prompt conversation history tool definitions tool outputs Key Principle Context engineering is about: → managing all the information your agent processes within its limited space
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Understanding AI Agent Security Fundamentals Introduction AI agents are powerful—but with that power comes responsibility. Security must be intentionally designed, especially when agents process files, access tools, and interact with users. Two Main Vulnerability Categories • Knowledge File Vulnerabilities • Agent Interaction Vulnerabilities
