AI Agents Playbook — Design Patterns & Starter Manifests

AI Agents Playbook — Design Patterns & Starter Manifests

For a one-person business, the best agents are small, predictable, and observable: they save time on routine tasks while keeping you firmly in control. This playbook gives compact design patterns, starter manifests you can copy, and a practical safety & testing checklist so you can run safe experiments and measure real value.

When to use a small AI agent

• Tasks that repeat regularly and follow a consistent structure (research, outreach, content repurposing).
• Work that benefits from a fast, low-cost assist plus a human checkpoint before final action.
• Situations where automation reduces friction but a failed decision wouldn’t be catastrophic.

Core design rules (short)

1. Small scope: design each agent for a single, well-defined outcome.
2. Human-in-the-loop: require a human approval step for external actions (sending emails, posting content, billing decisions).
3. Privacy by design: limit data access, mask or strip PII, and record what the agent saw and produced.
4. Idempotency: make repeated runs safe or detect duplicates.
5. Observability: log inputs, outputs, run time, cost, and failures; expose a simple status dashboard or report.

Pattern 1 — Research Assistant

Use when you need fast, structured background research (competitors, customer pain points, market stats).

What it does

Collects sources, summarizes findings, proposes 3–5 testable hypotheses or action items, and packages a short brief for you.

Starter manifest (template)

• Name: Research Assistant — [topic]
• Trigger: Manual or scheduled (e.g., weekly brief)
• Inputs: topic string, target audience, URL list (optional), time window
• Outputs: HTML/markdown brief with sources, key insights, top 3 opportunities
• Steps: fetch top sources, extract facts, cluster findings, generate summary, highlight uncertainties
• Human checkpoint: review brief before distribution — approve/edit
• Privacy constraints: do not include PII from scraped sources; redact emails/phone numbers
• Monitoring: runs/period, average runtime, brief approval rate, false-positive or hallucination reports

Example manifest (JSON sketch)

{
  "name": "research-assistant-topic",
  "trigger": "manual",
  "inputs": {"topic":"string","audience":"string","seedUrls":"[url]"},
  "outputs": {"brief":"markdown","sources":"[url]"},
  "humanCheckpoint":"review_before_publish",
  "privacy":"strip_pii",
  "metrics":["run_time","approval_rate"]
}

Pattern 2 — Outreach Assistant

Use when you want to scale personalized outreach (cold leads, follow-ups, partner intro sequences).

What it does

Generates personalized messages from a template, performs lightweight validation, and hands off to you for approval or scheduling.

Starter manifest (template)

• Name: Outreach Assistant — [audience]
• Trigger: CSV upload or workflow event
• Inputs: contact row (name, org, context), outreach goal, template variants
• Outputs: personalized message drafts, suggested subject lines, risk score
• Human checkpoint: approve each batch before sending
• Privacy constraints: do not store contact PII beyond retention window; use encrypted storage for credentials
• Failure mode: ambiguous contact data → flag for manual review

Quick governance rule

Never let an agent send messages directly to customers without a verified human approval step and an audit trail.

Pattern 3 — Content Repurposer

Use when you want to turn one piece of content into multiple formats (blog, tweet thread, newsletter blurb, image caption).

Starter manifest (template)

• Name: Content Repurposer — [source id]
• Trigger: new content published or manual upload
• Inputs: source content (URL or text), tone, target formats
• Outputs: drafts for each format + suggested posting schedule
• Human checkpoint: approve edits and schedule posts
• Privacy: if source contains client info, require explicit consent before republishing

Generic manifest checklist (copy for any agent)

1. Name & short purpose
2. Trigger (manual, scheduled, event-driven)
3. Inputs (types & examples)
4. Outputs (return types & storage location)
5. Clear human checkpoints
6. Privacy & data retention policy
7. Credentials & access required
8. Observability: logs, run id, metrics to collect
9. Failure modes and safe fallback
10. Estimated cost per run and rate limits

Testing & Safety Checklist

• Run in a sandbox with synthetic data first.
• Create a validation dataset with known edge cases.
• Force common failure modes and ensure the agent fails closed (doesn't send or act).
• Record the agent decision trace (prompt, model response, post-processing) for audits.
• Confirm human approval steps are required and tested end-to-end.
• Monitor for hallucinations: keep a simple report where you mark any hallucination and adjust prompts.

Observability & Metrics to track

• Runs per day/week
• Success rate (no human rework)
• Approval time (how long human checkpoints take)
• Cost per run and cumulative cost
• Time saved estimate (before vs after)
• Incidents (wrong sends, privacy incidents, hallucinations)

Maintenance & Lifecycle

Make a short maintenance plan:

• Weekly review of recent outputs (spot check 5–10).
• Monthly prompt tuning and metric review.
• Versioned manifests with changelogs (so you can rollback).
• Quarterly audit for privacy & credential rotation.

When NOT to use an agent

• High-stakes legal, clinical, or safety decisions.
• Tasks requiring deep, contextual judgment or nuanced human relationships without strong oversight.
• When data access would violate client agreements or regulations.

Starter experiments (ship small, learn fast)

1. Research pilot: run a research agent on one niche topic; success = a brief that leads to one testable marketing idea within two weeks.
2. Outreach pilot: generate 50 personalized outreach drafts, human-approve and send 20; success = at least 2 meaningful replies.
3. Content repurpose pilot: take 10 existing posts, produce 3 repurposed formats each, schedule the top 5; success = measurable engagement lift on one channel.

Next practical steps

1. Pick one pattern and copy the manifest checklist above into your workspace.
2. Run one sandboxed experiment with synthetic data and the testing checklist.
3. Measure cost and approval time. If the time saved justifies it, iterate on prompts and automation boundaries.

Resources & Templates

Use the generic manifest checklist as a starting template. If you want an interactive manifest builder, consider turning each manifest field into a short form so you can store, version, and reuse manifests across experiments.

Be pragmatic: a tiny, well-governed agent that saves you 30 minutes a week is more valuable than a grand, brittle automation that demands constant fixes.