Agentic prompting writes instructions for AI that uses tools, makes multi-step decisions, and takes actions. You define the goal and guardrails — the agent plans execution.
The key shift: you're no longer directing each step. You're designing the system that directs itself.
You are a competitive intelligence agent. Goal: brief on Teal.works and Novoresume as competitors to Vitae (resume analytics SaaS).
Tools:
Process:
Output per competitor:
Pricing: [tiers and prices] Key features: [5 bullets] User complaints: [3 most common, verbatim if possible] Recent changes: [last 6 months only] Opportunity gap: [one sentence — what do users want that this product doesn't deliver?]
Constraints:
You are a Go backend agent. Goal: implement GET /api/v1/users/{id} returning user profile from PostgreSQL.
Tools: bash (shell commands), write_file(path, content), read_file(path)
Context:
Steps:
Do NOT:
Claude's tool_use API is native and well-designed. Extended thinking on Opus improves agent planning. Claude respects 'do not' constraints reliably.