菜单
Design and implement autonomous AI marketing agent systems using the PRAL, BDI, and OODA frameworks. Invoke when a client is ready to move beyond reactive GenAI prompting to proactive, autonomous marketing workflows, or when planning an AI-first marketing operations architecture.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | ai-agentic-marketing-workflows |
| description | Design and implement autonomous AI marketing agent systems using the PRAL, BDI, and OODA frameworks. Invoke when a client is ready to move beyond reactive GenAI prompting to proactive, autonomous marketing workflows, or when planning an AI-first marketing operations architecture. |
SKILL.md; do not skip mandatory steps or required fields.references/ directory is added later, treat its files as the deeper source material and keep this SKILL.md execution-focused.Design and document autonomous AI marketing agent systems that perceive their environment, reason about what action to take, act without human prompting, and learn from outcomes. Output is an architecture specification, a chosen workflow template with full HITL safeguards, and a wave-appropriate implementation plan.
This skill assumes the client has completed ai-readiness-diagnostic and has
an AI maturity wave score (1, 2, or 3). Do not recommend Wave 3 architecture
to a Wave 1 client without a phased roadmap.
Ask for all of the following before generating any output:
ai-readiness-diagnostic; estimate if not available)Source: Nayebi (2025)
Most clients conflate generative AI with agentic AI. Clarify the distinction before designing any architecture.
Generative AI is reactive. It waits for a human prompt, generates output, then stops. Every action requires a human to initiate the cycle. This is Wave 1 and Wave 2 behaviour.
Agentic AI is proactive. It monitors its environment continuously, reasons about what action to take, executes that action, and updates its behaviour based on outcomes — without waiting for a human prompt. This is Wave 3 behaviour.
This distinction determines the architecture, the tools, the data requirements, and the risk controls needed. A business without clean engagement data or developer resource is not ready for a full agentic stack.
Wave guidance:
Most East African businesses should reach Wave 2 (Predictive ML, with 3+ months of clean data) before building Wave 3 agents.
Source: Nayebi (2025)
Every agentic system is built on the PRAL loop: Perceive → Reason → Act → Learn
Map the client's chosen workflow to each stage before recommending tools.
| Stage | What the agent does | Marketing example |
|---|---|---|
| Perceive | Gathers data from its environment | Scans social mentions, reads engagement metrics, receives inbound WhatsApp messages |
| Reason | Processes data and decides what to do — using an LLM or rule-based logic | Classifies sentiment, identifies a content gap, detects a campaign underperforming |
| Act | Executes the decision | Drafts content, sends an alert, triggers a campaign boost, routes a message to a human |
| Learn | Updates its behaviour based on outcomes | Feeds performance data back into the next Perceive cycle; adjusts thresholds and templates |
Apply the PRAL loop explicitly when designing each workflow template. Label each step so the client can see where human oversight sits.
Source: Nayebi (2025)
The BDI model — Beliefs, Desires, Intentions — maps naturally to marketing strategy and is the clearest way to define an agent's decision boundary.
| Component | Definition | Marketing application |
|---|---|---|
| Beliefs | What the agent knows | Audience data, engagement history, brand guidelines, competitor positions, product catalogue |
| Desires | What the agent is trying to achieve | Business goals — leads, awareness, retention, revenue — expressed as KPIs |
| Intentions | How the agent plans to act | Campaign tactics, content formats, channel choices, timing rules, escalation thresholds |
Prompt to use with client:
"Specify your agent's Beliefs (what data it has access to), Desires (what KPI it optimises for), and Intentions (what actions it can take). This defines the agent's decision boundary."
Document the BDI model before selecting any tool. An agent without a defined decision boundary will act unpredictably.
Borrowed from military strategy (Boyd, 1976), OODA is the fastest decision loop applicable to marketing agents operating in real-time social media environments.
Observe → Orient → Decide → Act
Faster OODA cycles = competitive advantage in fast-moving social media environments where a delayed crisis response or missed trend costs engagement.
Social listening agent example:
OODA complements PRAL: PRAL describes the agent's architecture; OODA describes the speed and logic of its decision-making in a single cycle.
Select the template that matches the client's target workflow. Fully specify the chosen template before recommending tools.
What it does: Automates the content creation and publishing pipeline from trend detection to post-performance feedback.
| Element | Detail |
|---|---|
| Trigger | Scheduled (daily/weekly) or event-driven (trending topic detected) |
| Actions | 1. Monitor trending topics and competitor content · 2. Generate draft content (caption, hashtags, image brief) · 3. Route draft to human for approval · 4. Publish approved content at optimal time · 5. Monitor post performance for 48 hours |
| HITL point | Human approves every draft before publishing — no autonomous publishing without review |
| Learn step | Performance data (reach, engagement rate, saves) fed back to refine future prompts and posting times |
| Tools | Claude API (drafting) + n8n or Make.com (orchestration) + Buffer/Hootsuite (scheduling) |
| EA feasibility | High — Wave 2 clients can implement with no-code tools |
What it does: Continuously scans social mentions, classifies sentiment, and alerts the team when a threshold is crossed.
| Element | Detail |
|---|---|
| Trigger | Continuous (hourly scan) or keyword-event (brand name mentioned) |
| Actions | 1. Scan Facebook, Instagram, X/Twitter, Google reviews for brand mentions · 2. Classify mention: positive / neutral / negative / crisis · 3. Log all mentions in dashboard · 4. Alert team when negative threshold crossed (e.g., 3+ negative mentions in one hour) · 5. Suggest pre-approved response options |
| HITL point | Human selects and sends response — agent does not post responses autonomously |
| Learn step | Mis-classifications flagged by human; agent updates sentiment rules |
| Tools | Mention.com or Google Alerts (listening) + Claude API (classification) + n8n (routing) + WhatsApp Business API (alert delivery) |
| EA feasibility | High — Google Alerts + Claude API is accessible and low-cost |
What it does: Monitors engagement metrics and triggers a targeted response campaign when performance drops below threshold.
| Element | Detail |
|---|---|
| Trigger | Metric threshold (engagement rate drops below X%, or follower growth stalls for N days) |
| Actions | 1. Pull platform analytics daily · 2. Compare against baseline benchmarks · 3. Detect underperformance · 4. Generate campaign response options (content boost, new format, re-engagement post) · 5. Present options to human for approval · 6. Execute approved option · 7. Report results after 7 days |
| HITL point | Human approves the campaign response before any content is published |
| Learn step | Successful response tactics stored; agent prioritises them in future recommendations |
| Tools | Platform analytics API + Claude API (analysis and drafting) + Make.com (orchestration) + Buffer (publishing) |
| EA feasibility | Medium — requires Wave 2 data maturity and API access to platform analytics |
What it does: A team of specialised agents collaborates to produce the monthly performance report with minimal human effort.
| Element | Detail |
|---|---|
| Trigger | Scheduled (last day of the month) |
| Actions | 1. Data agent — pulls platform statistics from all active channels · 2. Analysis agent — identifies patterns, anomalies, and top-performing content · 3. Writing agent — drafts narrative report with insights and recommendations · 4. Human consultant — reviews, edits, and presents to client |
| HITL point | Human reviews the full draft before delivery; no automated client-facing report |
| Learn step | Human edits tracked; writing agent refines its narrative style and recommendation quality |
| Tools | Platform APIs (data) + Claude API (analysis and writing) + n8n (orchestration) + Google Docs / Notion (output) |
| EA feasibility | Medium — high value but requires API access and developer setup for data pulls |
What it does: Classifies inbound WhatsApp messages, routes them to the correct response path, and handles routine enquiries autonomously.
| Element | Detail |
|---|---|
| Trigger | Inbound WhatsApp Business message received |
| Actions | 1. Receive and classify message (enquiry / complaint / order / other) · 2. Route to: decision tree (simple FAQ) / Claude API (nuanced enquiry) / human agent (complaint or high value) · 3. Respond or escalate · 4. Log interaction with timestamp and classification |
| HITL point | All complaints and high-value sales enquiries routed to human immediately; agent does not resolve complaints autonomously |
| Learn step | Mis-routed messages flagged; classification rules updated monthly |
| Tools | WhatsApp Business API + Claude API (classification and response drafting) + n8n (routing logic) |
| EA feasibility | High — WhatsApp penetration in EA makes this the highest-ROI agentic workflow for most clients |
Source: Nayebi (2025)
Every agentic workflow must define four safeguard components before going live. Include this section in every workflow specification delivered to the client.
1. Autonomous decision boundary Define what the agent can decide and act on without human review. Limit this to decisions that are: low-risk, routine, reversible, and within a defined value threshold (e.g., scheduling a post, classifying a mention, logging a message).
2. Escalation triggers Define what forces the agent to stop and wait for a human. Escalation is mandatory when a decision is: high-risk, irreversible (e.g., publishing to public), sensitive (crisis keywords, complaints, legal mentions), or above a value threshold (e.g., enquiry worth over UGX 500,000).
3. Escalation mechanism Specify: how the human is alerted (WhatsApp message, email, Slack), what information they receive (full context, agent's recommended options), the expected response time, and the override protocol if no response is received.
4. Audit trail Every agent action must be logged with: timestamp, action taken, data that triggered the action, reasoning or rule applied, and outcome. This log is reviewed monthly to improve agent performance and demonstrate accountability.
Match the roadmap recommendation to the client's current wave.
| Wave | Readiness criteria | What to build | Effort |
|---|---|---|---|
| Wave 1 — Automation | No analytics data required; any technical level | Zapier or Make.com automations that trigger AI content drafts on a schedule. Rule-based, no learning, no API calls. | 1–2 days setup |
| Wave 2 — Performance-triggered | 3+ months of clean engagement data; basic technical resource | Connect analytics data to AI for performance-triggered actions (e.g., engagement drop → draft new content). Requires platform data export or basic API access. | 1–2 weeks setup |
| Wave 3 — Full agentic | Clean data, developer resource, HITL safeguards in place | Full PRAL agents with continuous monitoring, LLM reasoning, and feedback loops. Requires API access, self-hosted orchestration (n8n), and ongoing maintenance. | 4–8 weeks minimum |
Do not propose Wave 3 to a Wave 1 client without a phased roadmap that moves them through Wave 2 first.
Recommend tools based on the client's technical resources and budget.
| Tool | Role in agentic stack | EA accessibility | Approx. cost |
|---|---|---|---|
| Claude API | LLM reasoning layer — classification, drafting, analysis | Yes — API account required | Pay-per-token |
| n8n | Workflow orchestration (self-hostable, open source) | Yes — developer resource needed for self-hosting | Free (self-hosted); from $20/month (cloud) |
| Zapier AI | No-code workflow automation with AI steps | Yes — browser only, no dev required | Free tier; from $19.99/month |
| Make.com | Visual no-code workflow builder | Yes — browser only, no dev required | Free tier; from $9/month |
| Hootsuite / Buffer | Publishing and scheduling layer | Yes — widely used in EA | From $15/month |
| Brandwatch / Mention | Social listening layer for sentiment monitoring | Limited — pricing is a barrier for small clients | From $99/month |
| WhatsApp Business API | Inbound message routing and response | Yes — high EA penetration; via Meta or third-party | From $0 (first 1,000 conversations/month free) |
For Wave 1 clients with no technical resource: Zapier or Make.com + Claude (via ChatGPT or Claude.ai interface, not API) is the most accessible entry point.
For Wave 3 clients with developer resource: n8n (self-hosted) + Claude API is the recommended EA-feasible stack.
Output meets standard when it satisfies all of the following:
Social media strategy for healthcare organisations and medical professionals in East Africa — covering patient trust architecture, compliance with the Uganda Data Protection and Privacy Act 2019, content strategy for combating health misinformation, patient network engagement, and healthcare crisis communication. Invoke when the client is a hospital, clinic, health NGO, pharmaceutical company, public health department, or individual medical professional building a social media presence, developing a social media policy, or managing a reputational or clinical communication challenge.
Generates an operational guide and all supporting content — tool selection rationale, avatar design brief, scripts, use-case plans, and distribution protocol — for deploying AI avatar video at scale in B2B outreach, client communications, and social content production. Invoke when a client wants to use AI-generated video for personalised outreach or branded video content, when production time on talking-head video needs to be reduced, or when a scalable video communications system is being built.
Transforms AI-generated marketing drafts into brand-voiced, human-quality content by applying a structured quality-control process covering vocabulary, cultural localisation, factual accuracy, and voice consistency. Invoke immediately after any AI-assisted draft and before any client delivery — for captions, blog posts, email copy, strategy documents, and proposals.
Transform one piece of long-form content into 10 platform-ready assets using AI tools. Solves the content creation bottleneck for resource-constrained East African marketing teams. Invoke when a client needs more content output from their existing content investment.
Runs a structured pre-delivery audit of AI-generated content — copy, images, and personas — for cultural bias, Western default assumptions, and inaccurate cultural representation, with specific attention to East African and non-Western contexts. Invoke before any AI-assisted deliverable is sent to a client, whenever AI tools have generated content depicting people, communities, or cultural settings, or when an internal review flags that content feels generically "Western" or generically "African" rather than accurately localised.
Audit and prepare client data for AI marketing readiness. Produces a completed data hygiene checklist, a data mapping framework, and a 30-day remediation plan so that the client's data is clean and structured before any AI marketing tool is deployed. Invoke before any AI marketing tool deployment — this skill establishes the data quality foundation that all AI marketing activities depend on. Also invoke when a client reports inconsistent AI outputs, poor personalisation results, or chatbot errors.