ai-agentic-marketing-workflows

name	ai-agentic-marketing-workflows
description	Use when AI Agentic Marketing Workflows is needed to produce a workflow specification for social-media or digital-marketing work; use `ai-readiness-diagnostic` when its narrower outcome is requested.
metadata	{"portable":true,"compatible_with":["claude-code","codex"]}

AI Agentic Marketing Workflows

Use When

• Use this skill when the requested outcome is specifically a workflow specification and the supplied brief falls within ai agentic marketing workflows.

Do Not Use When

• Use ai-readiness-diagnostic when its narrower output is the real deliverable; do not use this skill as a generic substitute.
• Do not use it to publish, send, spend, alter a live account, or make unsupported legal, platform, performance, or certification claims.

Required Inputs

Artefact	Source/provider	Required?	If absent
AI marketing use-case brief, intended human control point and success measure	Requester or approved brief	Yes	Stop and request the missing decision context.
Brand voice, offer facts, constraints and approvals	Client source pack or authorised owner	Conditional	State assumptions; do not invent names, prices, results or approvals.
Performance, platform or research evidence used for claims	Traceable export, URL, document or named source	Conditional	Draft the narrowest reviewable version and flag the missing evidence.

Capability and Permission Boundaries

Drafting is permitted within the supplied brief. Publishing, sending, spending, changing live accounts, or claiming certification requires separate explicit authority. Minimum capabilities are read access to supplied files and search across the authorised evidence set. Use only the files, tools, accounts and evidence made available for the engagement, expose every unassessed check, and obtain explicit authority before any mutation.

Degraded Mode

Fallback: if files, network access, platform data, language review or production tools are unavailable, return the narrowest useful qualified workflow specification; mark unavailable checks not assessed and never convert them into a pass.

Decision Rules

Choice	Action	Failure or risk avoided
Data readiness, AI maturity and risk support the proposed operating level	Choose the lowest viable automation level and define its human approval gate.	Automating an unsafe or unevaluable marketing process.
A required fact or approval is missing	Stop that claim or action; request it or use an explicit placeholder.	Fabricated facts, implied consent or unauthorised publication.
Evidence is partial but a useful draft is possible	Deliver a qualified draft with gaps and the next verification step.	Treating an unassessed requirement as passed.

Workflow

1. Confirm the exact workflow specification, consumer, market, channel and approval boundary; route to ai-readiness-diagnostic if it is the closer match.
2. Inventory supplied facts, source provenance, constraints and missing inputs; stop if the objective, audience or authority is unknowable.
3. Select the domain method and record the material decision behind it before drafting.
4. Produce the smallest complete workflow specification; keep facts traceable and placeholders visibly unresolved.
5. Test the result against the decision table, domain quality criteria and anti-slop gate; recover by narrowing or qualifying unsupported portions.
6. Deliver the artefact with evidence, assumptions, unassessed checks and the next approval or verification step.
7. Apply AI campaign trust, control, correction, and drift before recommending any rollout or learning loop.

Outputs

Artefact	Consumer	Observable acceptance condition
Workflow specification	Requester, client reviewer or delivery team	The workflow specification addresses the named audience and objective, records assumptions, and passes the skill's domain checks without invented facts.
Decision and gap note	Approver or next workflow	Names the chosen route, evidence used, unresolved inputs and any action requiring authority.

Evidence Produced

Evidence	Format	Acceptance condition
Source/assumption register and completed release checklist	Inline table, checklist or linked source note	Every material claim, decision and unavailable check is traceable.

Quality Standards

• Preserve the domain guidance and East African market context below; replace it only when the requester names another market.
• Use British English unless the target language or market requires otherwise, and verify names, figures, quotations and platform rules before use.
• Make the key choice visible, cover failure and edge cases, and keep the result ready for its named consumer.
• Run the repository's anti-ai-slop ship gate; a blocking factual, cultural, safety or permission defect stops release.
• Separate the model, surrounding system, inputs, inferred inputs, outputs, human reviewer, and external action. Require specific disclosure, correction, escalation, drift monitoring, and a non-AI fallback.

Anti-Patterns

• Writing before the objective and audience are known. Fix: stop and obtain the missing brief fields.
• Reusing a neighbouring skill's template because the headings look similar. Fix: route by the requested workflow specification, not vocabulary overlap.
• Adding a price, result, quotation, platform limit or cultural claim without a traceable source. Fix: verify it or qualify/remove it.
• Treating missing access, evidence or native-language review as approval. Fix: mark the check not assessed and narrow the result.
• Publishing, sending, spending or changing a live account from drafting authority alone. Fix: obtain explicit action-specific authority and retain the approval record.
• Describing an AI workflow as autonomous without a decision boundary, human escalation, correction path, audit trail, or rollback. Fix: complete the trust/control/drift reference or withhold the rollout recommendation.

References

• ai-readiness-diagnostic is the nearest routing comparison for this skill.
• AI campaign trust, control, correction, and drift
• Repository agent guide defines the engine-wide market, safety and anti-slop gates.

Purpose

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.

Required Inputs

Ask for all of the following before generating any output:

1. Client business name — trading name and legal entity if different
2. Industry — sector, product or service type
3. Country / city — defaults to Uganda if not specified
4. Current AI maturity wave — Wave 1, 2, or 3 (from ai-readiness-diagnostic; estimate if not available)
5. Target workflow to automate — select one primary: content / sentiment monitoring / reporting / customer service / campaign optimisation
6. Available technical resources — none / basic (can use no-code tools) / developer (can call APIs and self-host)

Agentic vs Generative AI: The Critical Distinction

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:

• Wave 1 clients → rule-based automation (Zapier / Make.com triggers)
• Wave 2 clients → performance-triggered AI actions using analytics data
• Wave 3 clients → full PRAL agents with continuous monitoring and learning

Most East African businesses should reach Wave 2 (Predictive ML, with 3+ months of clean data) before building Wave 3 agents.

The PRAL Loop

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.

The BDI Model for Marketing Agents

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.

The OODA Cycle for Real-Time Decisions

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:

• Observe — scan all mentions of the brand across Facebook, Instagram, X/Twitter, and Google every hour
• Orient — classify each mention by sentiment (positive / neutral / negative / crisis) and topic category
• Decide — apply rules: respond autonomously to positive enquiries; escalate negative mentions; flag crisis keywords immediately
• Act — post pre-approved response template, or send alert to human via WhatsApp/email with full context

OODA complements PRAL: PRAL describes the agent's architecture; OODA describes the speed and logic of its decision-making in a single cycle.

Five Agentic Workflow Templates

Select the template that matches the client's target workflow. Fully specify the chosen template before recommending tools.

1. Content Pipeline Agent

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

2. Sentiment Monitoring Agent

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

3. Proactive Campaign Agent

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

4. Multi-Agent Reporting System

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

5. WhatsApp Response Agent

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

HITL Safeguard Design

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.

Three-Wave Implementation Roadmap

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.

Tool Stack Options

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.

Quality Criteria

Output meets standard when it satisfies all of the following:

• Client's current AI maturity wave is identified and a wave-appropriate architecture is recommended — no Wave 3 proposal for a Wave 1 client without a phased roadmap
• At least one agentic workflow template is selected and fully specified: trigger, PRAL mapping, actions, HITL point, learn step, and tools
• The PRAL loop is explicitly mapped for the chosen workflow — each stage labelled
• The BDI model is documented: Beliefs (data sources), Desires (KPI optimised), and Intentions (actions the agent can take)
• HITL safeguards are defined: autonomous decision boundary, escalation triggers, escalation mechanism, and audit trail
• Tool stack is recommended based on the client's technical resources and budget with EA accessibility noted
• EA feasibility is assessed — WhatsApp-based and no-code workflows prioritised for clients without developer resource
• Autonomy level, allowed-action table, eval set, run logging fields, and kill-switch owner are defined before any autonomous action is recommended

References

• Nayebi, F. (2025) Foundations of Agentic AI for Retail. Gradient Divergence.
• Venkatesan, R. and Lecinski, J. (2026) The AI Marketing Canvas, 2nd edn. Stanford University Press.
• Farri, E. and Rosani, G. (2025) HBR Guide to Generative AI for Managers. Harvard Business Review Press.
• references/agentic-marketing-operating-model.md - source-synthesised hardening rules for production marketing agents.