O Scorecard de Delegação de AI Agents: Decida quais tarefas delegar e quais manter
Porque é que este prompt importa
Teams that pick the wrong first task to delegate -- either something too risky, like client-facing communications with legal exposure, or something too trivial to demonstrate real value -- sour stakeholders on the entire initiative within one bad quarter. A poorly chosen pilot can stall agent adoption for a year; a well-chosen one builds the case for expanding scope.
Para que o usamos
You're a marketing operations manager who just got budget approval for an AI agent tool, and leadership wants a rollout plan by Friday -- but your team has 40 different recurring tasks and no clear way to decide which 5 to hand to an agent first.
Prompt
Act as an AI workflow consultant who specializes in helping knowledge workers and small teams decide which of their recurring tasks are good candidates for AI agent delegation, and which should stay manual. CONTEXT: - My role: [YOUR JOB TITLE / TEAM FUNCTION] - Recurring tasks I'm evaluating: [LIST 5-15 RECURRING TASKS, ONE PER LINE, WITH ROUGH TIME SPENT PER WEEK] - AI agent tools currently available to me: [LIST TOOLS, e.g. "Claude with file access, a Zapier AI agent, ChatGPT with browsing"] - My risk tolerance for this rollout: [LOW - client-facing/financial/legal exposure / MEDIUM - internal-facing but visible to leadership / HIGH - drafts and personal productivity only] TASK: For each task in my list, score it on two axes (1-5 scale): 1. COMPLEXITY: how much judgment, ambiguous context, or exception-handling the task requires (1 = fully rule-based, 5 = requires deep contextual judgment) 2. RISK: the cost of a wrong or low-quality output going uncaught (1 = low stakes, easily reversible, 5 = high stakes, hard to reverse or externally visible) Then recommend one of three actions for each task: - AUTOMATE FULLY: agent handles it end-to-end, spot-checked periodically - AUTOMATE WITH REVIEW: agent produces a draft/output, a human approves before it goes live - KEEP MANUAL: not a good current fit for agent delegation CONSTRAINTS: - Never recommend "Automate Fully" for any task with a risk score of 4 or 5, regardless of complexity score - Flag any task where the underlying data or context needed to do it well is not clearly documented anywhere -- that's a blocker independent of the complexity/risk score - Be realistic about current AI agent capability in [CURRENT MONTH AND YEAR] -- do not recommend automating tasks that require live judgment calls involving other people's feelings, legal interpretation, or irreversible financial commitments - If two tasks have similar scores, prioritize whichever is higher time-cost per week for the pilot recommendation OUTPUT FORMAT: 1. A table: Task | Complexity (1-5) | Risk (1-5) | Recommendation | One-line reasoning 2. A ranked list of the top 3 tasks to pilot first, with a one-sentence explanation of why each is a good starting point 3. One task from the list that should explicitly NOT be automated yet, with a clear explanation of what would need to change (better documentation, lower stakes, proven agent track record) before revisiting it
Resultado
Delegation Scorecard for: Marketing Operations Manager
| Task | Complexity | Risk | Recommendation | Reasoning |
|---|---|---|---|---|
| Weekly social media scheduling from content calendar | 2 | 2 | Automate Fully | Rule-based, low stakes, easily corrected if wrong |
| First-draft blog posts from briefs | 3 | 2 | Automate with Review | Good first drafts possible, but brand voice needs a human pass |
| Client campaign performance reports | 2 | 3 | Automate with Review | Data pull is mechanical, but client-facing framing needs a sanity check |
| Responding to customer complaints on social media | 4 | 5 | Keep Manual | High visibility, reputational risk, requires emotional judgment |
| Competitor pricing page monitoring | 1 | 1 | Automate Fully | Simple scraping and diffing task, no judgment required |
| Ad spend budget reallocation between channels | 4 | 5 | Keep Manual | Financial commitment risk exceeds agent trust threshold |
| Internal weekly team status digest | 2 | 1 | Automate Fully | Low stakes, internal only, easy to spot-check |
Top 3 to Pilot First
- Competitor pricing page monitoring -- zero risk, immediate time savings (est. 3 hrs/week), builds team trust in agent output with no downside if imperfect.
- Weekly social media scheduling -- high time cost (5 hrs/week), fully rule-based once the content calendar exists, and errors are cheap to catch before a post goes live.
- Internal weekly team status digest -- internal-only audience means mistakes are low-cost, and it's a visible early win to show leadership the pilot is working.
Do Not Automate Yet
Responding to customer complaints on social media. This task combines the highest complexity and highest risk scores on the list: it requires reading emotional subtext, applying judgment about when to escalate versus de-escalate publicly, and any visible misstep becomes a reputational incident that's hard to walk back. Before revisiting, the team would need: (1) a documented decision tree for at least the 10 most common complaint types, (2) a mandatory human-approval gate before any agent-drafted response goes live, and (3) at least 90 days of agent-drafted-human-approved responses to build a track record before considering unsupervised operation on even the lowest-risk complaint category.
Toda equipe que está implementando AI agents em 2026 enfrenta o mesmo modo de falha silencioso: não é escolher um agente ruim, mas sim selecionar a primeira tarefa errada para delegar a ele. Entregue uma tarefa muito trivial ao agente e a liderança não enxerga valor. Entregue algo muito arriscado — comunicações com clientes, compromissos financeiros, qualquer coisa com exposição legal — e uma única saída ruim se torna a história que as pessoas lembram, matando o momentum de toda a implantação.
Este prompt resolve isso forçando uma comparação estruturada em vez de uma escolha baseada em intuição. Ele pontua cada tarefa recorrente em dois eixos independentes: complexidade (quanto julgamento e contexto ambíguo a tarefa exige) e risco (o custo de uma saída ruim passar despercebida). Esses dois eixos importam separadamente — uma tarefa pode ser simples mas de alto risco (aprovar uma grande mudança nos gastos com anúncios), ou complexa mas de baixo risco (redigir uma atualização interna de status que qualquer um pode corrigir rapidamente se estiver errada).
A restrição rígida embutida no prompt — nunca recomendar automação total para nada que pontue 4 ou 5 em risco, independentemente de quão simples pareça — é o que impede que isso vire apenas um carimbo para o que for mais conveniente. Ele também força uma resposta explícita sobre o que NÃO automatizar ainda, e o que precisa mudar especificamente antes de revisitar essa decisão. Essa é a parte que a maioria das equipes pula, e é a parte que evita um erro público doloroso seis semanas após o início da implantação.