agent-first-product-strategy
Reframe AI product and SaaS strategy from human-user assumptions to agent-first execution. Use when redefining product positioning, success metrics, API/docs priorities, go-to-market, or roadmap decisions for an AI-native market where agents are primary software users.
What this skill does
# Agent-First Product Strategy ## Overview Use this skill to turn high-level AI-era ideas into concrete product strategy, metric design, and execution choices. ## Workflow 1. Identify old-paradigm assumptions in the current plan. 2. Reframe target user and value unit for agent-first operation. 3. Redesign product surface around API, protocol, and documentation quality. 4. Replace vanity metrics with outcome and reliability metrics. 5. Propose phased execution with explicit tradeoffs. ## Step 1: Find Old-Map Assumptions Audit the current strategy for these legacy assumptions: - `DAU` as primary growth signal. - `tool -> community -> platform` as default path to defensibility. - Human-first UX as the dominant moat. - Attention-time capture as monetization logic. - "overseas expansion" as localization-first growth logic. If any assumption exists, mark it as a risk and quantify impact on cost, speed, or defensibility. ## Step 2: Reframe to Agent-First Define strategy with these agent-era premises: - Primary user can be `Agent`, not only human operators. - Core value is `outcome delivery efficiency` (time-to-outcome and quality), not time spent. - Product may be better positioned as `capability infrastructure` rather than consumer app. - Distribution can be `agent discoverability + machine-usable docs`, not only human marketing funnels. Return a one-line reframing statement: `We help <agent/human+agent segment> achieve <outcome> via <capability/API>, optimized for <speed/reliability/cost>.` ## Step 3: Define Product Surface Prioritize product work in this order: 1. API clarity and stability (`auth`, schema consistency, error model). 2. Documentation quality (machine-readable examples, clear contracts, rate limits, versioning). 3. Protocol interoperability (standard interfaces, predictable retries, idempotency). 4. Reliability layer (latency, success rate, graceful degradation, observability). 5. Human UI as a control surface, not the only surface. When tradeoffs are hard, prefer decisions that improve repeatable agent invocation quality. ## Step 4: Replace Metrics Convert success metrics from attention-era to productivity-era: - Replace `DAU/time spent` with `task completion rate`, `unit outcome cost`, and `end-to-end delivery time`. - Track `API success rate`, `P95 latency`, `agent repeat-call ratio`. - Track `first-call success` (agent can integrate correctly on first attempt). - Track `integration lead time` (from docs read to first production call). Read `references/agent-first-metrics.md` to choose metric formulas and guardrails. ## Step 5: Build Execution Plan Produce a phased plan: 1. `0-30 days`: fix integration blockers, tighten API contract, publish minimal docs set. 2. `31-90 days`: improve reliability/SLOs, ship agent onboarding examples, cut integration time. 3. `90+ days`: optimize cost-performance frontier, deepen protocol ecosystem, create domain moats. For each phase include: - Goal - Top 3 actions - Metric target - Major risk and mitigation ## Output Format When responding, output in this structure: 1. Current assumptions detected 2. Agent-first reframing statement 3. Product surface priorities 4. Metric redesign table 5. 30/90/+ day plan 6. Top unresolved strategic question
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