Moving the Malaysian marketing & creative agency from human-prompted AI assistants to a coordinated team of agents that runs new-business, delivery, and creative workforce on a daily schedule — and delivers a ranked decision list to the CEO every morning.
Prepared forAgency CEO & Partners
FormatOnline Reference
Case StudyMid-sized Integrated Marketing Agency · Kuala Lumpur & Penang
Your AI investment will not pay back until the AI stops waiting to be asked.
Most Malaysian agency deployments still treat AI as a faster prompt box — a tool that produces value only when a strategist or creative pulls it. The next operating model inverts that: a team of specialist agents consumes the agency's data on a fixed daily schedule, weighs the trade-offs (pitch-win probability, senior-to-junior mix, account margin, burnout signals, PDPA on client data), and delivers a ranked, ready-to-approve Decision List to the agency CEO every morning.
The ShiftFrom copilots to a coordinated team of agents
The Problem
AI is reactive
Copilots and dashboards still require a human to formulate the question, pull the data, and synthesise the answer. Decision speed = human speed.
The Shift
From pull to push
Agents run on a schedule. They watch for variance, run the scenarios, and surface decisions before the executive knows to ask.
The Prize
Compounding daily edge
Decisions that used to take a week of cross-functional meetings arrive pre-staffed, pre-modelled, and ranked by RM-impact every morning at 06:00 MYT.
Slide 2 — Governing ThoughtAITraining2U · The Agentic Operating Model
The Paradigm Shift
03 / 13
Current State vs. Target State
Two operating models. Only one scales beyond the executive's calendar.
Today — Reactive AI
Humans pull. AI answers.
Trigger — A human notices a problem or asks a question.
Cadence — Ad-hoc; bounded by manager attention.
Synthesis — Performed in the analyst's head, slide deck, or spreadsheet.
Coverage — Whatever the executive thought to look at this week.
Output — A chart, a summary, a "this looks worth investigating."
Bottleneck — The bandwidth of the most expensive person in the room.
Target — The Agentic Operating Model
Agents push. Humans approve.
Trigger — A scheduled run (e.g., 04:00 MYT daily), or a signal crossing a pre-set threshold.
Cadence — Continuous; the team of agents never sleeps.
Synthesis — A dedicated Orchestrator agent does it before the executive opens their laptop.
Coverage — Every account, pitch, project, creative, and licence — every day.
Output — A ranked Decision List with modelled RM-impact and one-click approval.
Bottleneck — Removed. The executive becomes a judge, not a query.
The implication for the C-suite: the unit of work shifts from "running the business" to "ratifying the decisions the business has already surfaced." Span of control expands by an order of magnitude.
Slide 3 — Paradigm ShiftAITraining2U · The Agentic Operating Model
Case Study · Malaysia
04 / 13
A worked example — mid-sized Malaysian marketing & creative agency
A Malaysian business where every variable moves every hour. The ideal stress-test.
A mid-sized integrated agency in Malaysia compresses every operational discipline of a professional-services firm into a single billable day: pitch-effort allocation against the next RFP, perishable senior-creative hours under tight client deadlines, freelance ramp-and-cut governed by the Employment Act 1955 and EPF / SOCSO, PDPA obligations on client data, creative reputation governed by the Communications and Multimedia Act, and demand driven by client launch calendars, festive campaign seasons, and CMO budget cycles. Multiplied across a portfolio of ~80 active accounts and ~120 staff between Kuala Lumpur, Petaling Jaya, and Penang, no founding partner can hold the full state of the firm in working memory.
Pitches in flight · burn-rate tracking · client launch cadence
The five operational tensions the team of agents must hold simultaneously
Tension 1
Margin vs. Utilisation
Push utilisation to 85% for cash today, or hold senior bandwidth for the next big pitch?
Tension 2
Quality vs. Speed
Ship the on-brief draft Friday, or hold for a Monday round that wins the next RFP?
Tension 3
Senior vs. Junior mix
Staff the account with a senior creative, or a mid-weight with senior oversight?
Tension 4
New-business vs. Account-care
Chase the RFP that lands tomorrow, or protect the retainer that pays the lights?
Tension 5
Scale vs. Selectivity
Open a 4-person pod on the new account, or cull the bottom-decile retainer that burns hours?
Slide 4 — The CaseAITraining2U · The Agentic Operating Model
The Team of Agents
05 / 13
Five specialists, one chief of staff
Each agent owns one operational tension. The Orchestrator owns the trade-off between them.
Think of each agent as a digital department head who never sleeps, never takes leave, and reads every account's data every hour. The chips at the bottom of each card show the kind of expertise the agent embodies — detailed on the next slide.
Pitch Agent
New-Business & Pitch
The Pitch Strategist
Watches: live RFP pipeline, competitor agency activity, sector launch calendars, win-rate by vertical, pitch-effort burn.
Decides: which RFPs deserve senior bandwidth, the win-probability score, and the 90-day pitch pipeline everyone else plans against.
RFP scoringPipeline forecasting
Delivery Agent
Delivery & Project Management
The Project Throughput Manager
Watches: live project plans, burn-rate, SLA breaches, scope-creep flags, dependencies on freelancer pool and vendor stack.
Decides: which projects need re-planning, scope renegotiation, or escalation; auto-drafts the resource booking changes.
Project planningBurn-rate tracking
Creative Agent
Creative & Workforce
The Talent Allocator
Watches: creative/strategy/AM availability, skill match per brief, OT cap, EPF / SOCSO, freelancer pool, burnout signals.
Decides: the 14-day allocation plan — compliant with the Employment Act, balanced for utilisation and senior-junior mix.
Talent allocationBurnout signals
Studio Tech Agent
Tech & Studio Tools
The Production Stack Manager
Watches: Adobe / Figma / generative-AI licence usage, DAM activity, studio uptime, vendor reliability, PDPA NDA chain.
Decides: which licences to consolidate, which assets to retire from the DAM, which vendor risks to escalate before they bite a live project.
Tool licensingPDPA compliance
Account Agent
Account Portfolio Performance
The Client-Network Strategist
Watches: margin per account, retainer trajectory, NPS, share-of-wallet, whitespace, and what the other agents report.
Decides: classifies every account as Overperform / On-Target / Underperform vs. its peer cohort, and triggers the right tier action (promote, retain, cull).
Portfolio analyticsAccount cohort scoring
Chief of Staff
Chief of Staff
The Synthesis Layer
Watches: what Pitch Agent, Delivery Agent, Creative Agent, Studio Tech Agent and Account Agent are recommending, plus the agency P&L and cash flow.
Decides: reconciles conflicts, ranks the day's calls by expected RM-impact, and presents the shortlist to the agency CEO.
Decision synthesisCausal attribution
Slide 5 — The Team of AgentsAITraining2U · The Agentic Operating Model
The Build Team
06 / 13
What sits inside each agent
The skill stack. Each agent bundles a named set of business capabilities.
An agent is not one trick — it is a stack of discrete business capabilities working together. Column 2 lists the capabilities you must build (or already partly run today, fragmented across functions). Column 3 tells you who to hire. Column 4 is the promise each agent makes to the others — the contract that lets the team operate as a team, not as a collection of dashboards.
Demand-to-talent matchingConverts the Delivery Agent project plans into named creatives by skill, by week.
Compliant allocationEmployment Act 1955 · EPF · SOCSO · OT cap · weekly rest day — hard-coded.
Skill-mix matchingRight senior-to-junior ratio per account; freelancer pool for spillover.
Utilisation balancingTarget 70–80% billable, prevents overload on senior creatives.
Burnout signal detectionPatterns in OT, leave deferral, late check-ins — flagged early.
Head of creative resourcing · talent ops lead · HR-tech integrator.
"Here is the cheapest legal allocation that staffs every live brief for the next 14 days — and the people whose burnout signals need a retention call."
Studio Tech AgentTech & Studio ToolsThe Production Stack Manager
Licence consolidationAdobe, Figma, generative-AI, DAM, social-listening — true active seats vs. paid.
Studio uptime monitoringRender farm, edit suites, server-side AI tools — failure risk per week.
DAM curationTags, retires, and chases sign-offs on creative assets at scale.
PDPA & NDA chain validationEvery client asset has a valid PDPA basis and contractor NDA.
Vendor reliability scoringProduction house, post house, freelancer platforms — track record vs. brief.
Studio ops lead · IT / tech-ops engineer · PDPA / compliance officer.
"Here are the licences to retire, the studio assets at failure risk this week, and the vendor / PDPA risks to escalate before they bite a live project."
Peer cohort matchingGroups accounts by sector, fee band, retainer maturity — apples to apples.
Composite performance scoringMargin %, NPS, share-of-wallet, growth trajectory, hours-vs-fee — rolled into one score.
Tier classificationOverperform · On-Target · Underperform vs. true peers.
Whitespace & cross-sell IDWhere the network can grow inside an existing client.
Promotion & cull diligenceSurfaces the evidence pack for promoting a Tier-2 account, or culling a chronic loss-maker.
Head of client services · portfolio strategist · analytics lead with causal background.
"Of the ~80 active accounts, here are the Overperformers to promote to a dedicated pod, the Underperformers to fix or cull, and the cross-sell proven on similar clients before."
Chief of StaffChief of StaffThe Synthesis Layer
Multi-criteria decision rankingWeighs RM-impact, confidence, risk, strategic fit.
Conflict reconciliationWhen Pitch Agent wants senior creatives on a pitch but Delivery Agent needs them on a live burn — adjudicates.
Risk-appetite calibrationLearns the agency CEO's actual risk tolerance from approval history.
Causal attribution to source agentEvery recommended call traces to the agent that surfaced it.
Executive narrative draftingThe one-sentence "why" the CEO sees on each decision.
Decision-science lead · chief of staff with analytics fluency · senior orchestration engineer.
"Here are the 3–5 calls only you should make today, ranked by RM-impact, with the reasoning and the source agents."
How to read this slide: column 2 is the skill stack — the named business capabilities the agent automates. Many of these capabilities exist somewhere in your org today, fragmented and run monthly. The shift is bundling them into one agent that runs them daily, together. Column 3 is the small, cross-functional team that builds and owns it. Column 4 is the contract that makes the agents a team, not six dashboards.
Slide 6 — The Build TeamAITraining2U · The Agentic Operating Model
Architecture
07 / 13
The Information Pipeline
The diagram is the operating model. Read it top-to-bottom.
Raw signals feed the five specialist agents. The specialists' outputs cascade — Pitch Agent's pitch pipeline shapes Creative Agent's allocation; Delivery Agent's burn-rate shapes Account Agent's tier calls. The Orchestrator consumes all five and emits one artefact: a ranked decision list for the agency CEO.
Layer 1 · Raw Signals (refreshed every 15 minutes to 24 hours)
3–5 ranked, RM-quantified decisions awaiting one-click approval (see slide 10)
Human-in-the-loop
Raw signal source Specialist agent Orchestrator (synthesis) Executive decision artefact
Slide 7 — Architecture DiagramAITraining2U · The Agentic Operating Model
Unified Data & Memory
08 / 13
How the sub-agents feed the master — and what binds them together
Five sub-agents feed one master. All six share one unified data & memory layer.
The five specialists are not isolated silos. They all read from — and write back to — a single shared layer of data, memory, and learned context. That shared layer is what lets the swarm behave as one coherent operating system, not six dashboards on a Teams channel.
Sub-agent (specialist)
Master agent (orchestrator)
Shared operational data
Shared long-term memory
Slide 8 — Unified Data & MemoryAITraining2U · The Agentic Operating Model
The Information Cascade
09 / 13
How the agents talk to each other
The agents are not peers — they are linked. Each link is where the leverage lives.
The diagram on slide 7 shows the wiring. This slide shows the conversation — exactly what each agent tells the next, in plain English. No human team has ever held this whole conversation end-to-end. That is the point.
FromPitch Agent · Pitch Strategist
→tells
ToAccount Agent · Client-Network Strategist
"RFP from a regional bank lands tomorrow. Win-probability 62%. Pull the BFSI case studies and surface the 3 senior creatives with finance experience — and tell me which existing accounts are in the same sector before we agree to pitch."
FromAccount Agent · Client-Network Strategist
→tells
ToDelivery Agent · Project Throughput Manager
"Account X is now top-decile margin against its peer cohort. Promote to Tier-1 with a dedicated 4-person pod. Free up ~6 hrs/week of senior creative time from the saturated Account Y — Delivery Agent, re-plan accordingly."
FromDelivery Agent · Project Throughput Manager
→tells
ToCreative Agent · Talent Allocator
"Account Z brief due Friday is 38% over-budget on hours. Either we swap in a senior copywriter to close the brief faster, or we renegotiate scope with the client by Wednesday. Hold the freelancer pool for the BFSI pitch, not this fire."
FromCreative Agent · Talent Allocator
→tells
ToDelivery Agent · Project Throughput Manager
"Three mid-weight designers are showing burnout markers — late check-ins, leave deferral, OT spikes. Reduce concurrent project load from 4 to 2 each. I'm pulling 2 freelancers for the next 8 weeks to absorb spillover; cost the projects accordingly."
FromStudio Tech Agent · Production Stack Manager
→tells
ToCreative Agent · Talent Allocator
"Adobe licence expansion request for Account Z — bring DAM access to 4 new contractors. PDPA NDA chain is validated end-to-end; cost is approved within policy. Onboard them on the studio stack before Monday."
FromChief of Staff · Chief of Staff
↺loops to
ToAll agents
"Last week's pitch-effort reallocation lifted the pitch-win rate from 18% to 34%. Continue prioritising Pitch Agent's top-quintile RFPs; Account Agent, re-score the sector mix this week. The CEO's revealed risk appetite is now visibly higher on BFSI — recalibrate."
Slide 9 — The Information CascadeAITraining2U · The Agentic Operating Model
The Loop
10 / 13
Why this compounds — and a reactive copilot does not
Every approved decision teaches the system how to be smarter tomorrow.
09:00 onwards · Decisions execute through existing systems (project management, time-tracking, HRIS, finance, DAM).
23:59 · Outcome data flows back as ground truth. Pitch wins, burn-rate, and account tier moves are scored.
Why It Compounds
The reactive copilot has no memory of yesterday's bet
Forecast scoring — every prediction is measured against what actually happened. Drift is detected and the agent self-corrects.
Risk-appetite learning — every CEO approval teaches the Orchestrator how aggressive the boss really is, not what the policy doc says.
Playbook validation — Account Agent only triggers a tier action when matched-peer evidence says it has worked before. Each triggered action retrains the evidence base.
Compounding edge — Year 1 you replace status meetings. Year 2 the system out-forecasts the partner team that used to run them.
Executive takeaway: a reactive copilot is a productivity tool — it makes today faster. The agentic operating model is a learning institution — it makes tomorrow better than today, on schedule, without anyone asking.
Slide 10 — Optimisation LoopAITraining2U · The Agentic Operating Model
The Artefact
11 / 13
What the CEO actually opens at 06:00 MYT
The Daily Prioritised Decision List.
Five decisions, ranked by expected RM-impact and risk. Each pre-staffed across new-business, delivery, creative workforce, studio tools, and account-portfolio tier. The agency CEO judges the trade-off — the answer is already assembled.
Account Agent · Account portfolio tier snapshot · every account benchmarked against its peer cohort today
OverperformTop decile
12accounts
Trigger: promote the matched Tier-2 cohort to a dedicated 4-person pod — modelled +10pp margin lift per account.
On-TargetMiddle band
60accounts
Trigger: maintain and tune. A small cluster approaching the upper band — pre-qualified for the Overperform pod model next cycle.
UnderperformBottom decile
8accounts
Trigger: intervention plans (scope renegotiation, lead change) and cull reviews where the account has underperformed against its peer cohort for 12 consecutive weeks.
#
Recommended decision
Modelled impact
Source agents
Action
1
Repitch the FMCG retainer with an insight-led narrative · add Klang Valley shopper-cohort data
Pitch Agent: pitch-win lift modelled at 78% if the Klang Valley shopper-cohort data is folded into the deck. Account Agent confirms the account is currently mid-tier with strong margin headroom — uplift to top tier worth ~RM 480k margin/yr at retainer maturity.
+RM 980k year-1High confidence
Pitch Agent · Account Agent
P0Approve
2
Reallocate 6 senior creatives from saturated retail accounts to the new BFSI pitch
Delivery Agent flags 18% over-utilisation across the retail cluster — burn is already overrunning fees. Creative Agent confirms the BFSI pitch is under-staffed from Friday onwards; freelance pool covers the retail spillover at lower senior cost.
+RM 540k pitch captureMedium-high confidence
Delivery Agent · Creative Agent
P0Approve
3
Portfolio action — promote 2 mid-tier accounts to Tier-1 with a dedicated 4-person pod each
Account Agent: both accounts sit in the top of their cohort on margin for 12 consecutive weeks; cohort-matched pod model lifts margin from ~18% to ~28% on similar accounts before. Pod absorbs hours within existing Creative Agent bench — no incremental headcount.
+RM 1.2M annualisedMedium-high confidence
Account Agent · Creative Agent
P0Approve
4
Switch 3 SaaS subscriptions to consolidated procurement
Studio Tech Agent: licensing audit shows 23% redundancy across Adobe, Figma, and a generative-AI tool — same seats counted twice across studios. Consolidation is policy-clean; no productivity loss against the active-seat baseline.
Creative Agent: late check-ins, OT pattern, leave deferral over 6 weeks. Account Agent: their accounts represent ~RM 12M of weighted pipeline. This is a retention conversation the agency CEO needs to have this week — not an automated allocation tweak.
−RM 1.8M if both leaveIf unresolved within 14 days
Creative Agent · Account Agent
EscRoute
Slide 11 — The Daily Prioritised Decision List · Illustrative · MalaysiaAITraining2U · The Agentic Operating Model
The Implementation Path
12 / 13
From a single Account Director to the full operating model
Four phases. Hire as you go. Right-size for your maturity.
Agencies don't begin at the agentic operating model — they walk there. Each phase adds agents, decision rights, and value. The entry bar matches where you actually are today, not the end-state you aspire to. Most Malaysian agencies should start at Phase 1 — a single co-pilot in one Account Director's pocket.
Phase 01 · AssistAccount Director Co-pilotMonths 0–2
One agent in the Account Director's pocket. A daily action checklist on their phone — not a dashboard, not a status meeting.
Entry bar — your starting maturity
A working PM tool and time-tracking. AD/PM team runs daily account stand-ups over Slack or WhatsApp.
Mode: Push-only. Daily 09:00 priority brief lands on the AD's mobile; the AD executes.
What the Account Director sees
A daily ranked checklist: client status risks, burn-rate alerts, scope-creep flags, next-best-pitch suggestions, must-do client touchpoints.
Illustrative first project
Roll out to 6 Account Directors across Kuala Lumpur and Penang offices, with the daily 09:00 priority brief.
Build team2 people
Phase 02 · CrawlFoundation PilotMonths 2–6
One specialist agent in advisory mode. One discipline. Prove the daily-push cadence works before scaling anything.
Entry bar — your starting maturity
Phase 1 in production across the AD pool. AD/PM teams have a daily completion habit.
Agents activated
Pitch AgentDelivery AgentCreative AgentStudio Tech AgentAccount AgentChief of Staff
Mode: Read-only / advisory. Agent recommends; the new-business team decides and executes manually.
What the CEO sees
A daily insights email at 06:00 MYT: 1–2 surfaced pitch opportunities, with win-probability and sector-mix commentary.
Illustrative first project
Pitch Agent as the pitch-strategy advisor for the new-business team — RFP scoring + sector-mix recommendations, scored daily against actual pitch outcomes.
Build team3 people
Phase 03 · WalkCoordinated OpsMonths 6–12
The operational trio. Agents start talking to each other and to existing systems — partners still approve every action.
Entry bar — your starting maturity
Phase 2 live and trusted. Executive used to daily insights. Small data team in place.
Agents activated
Pitch AgentDelivery AgentCreative AgentStudio Tech AgentAccount AgentChief of Staff
Mode: Coordinated. Pitch Agent's pipeline cascades into Delivery Agent's plans and Creative Agent's allocation. Actions auto-drafted; partners approve.
What the CEO sees
A weekly cross-agent scorecard plus same-day escalations when agents disagree or thresholds are crossed (burn-rate, pitch-conflict, burnout).
Illustrative first project
Quote-to-deliver loop on the top-15 retained accounts. Cascade goes live: pitch → project plan → creative allocation in one flow.
Build team8–12 people
Phase 04 · RunFull Operating ModelMonths 12–24
All sub-agents + the master orchestrator + the unified data & memory layer. The agency CEO opens the Daily Decision List at 06:00.
Entry bar — your starting maturity
Phase 3 producing measurable RM-lift on each agent. Cross-functional data team. Partners ready for one-click approval.
Agents activated
Pitch AgentDelivery AgentCreative AgentStudio Tech AgentAccount AgentChief of Staff
Mode: Full agentic operating model. Autonomous synthesis; CEO ratifies the daily list; system learns from every approval.
What the CEO sees
The Daily Prioritised Decision List (slide 11). 3–5 ranked, RM-quantified calls awaiting one-click approval.
Illustrative first project
Pan-agency operating system across all 80 accounts and 120 staff in KL, PJ, and Penang. Year 2: the system out-forecasts the partner team it replaced.
Build team15–20 people
Slide 12 — The Implementation Path · IllustrativeAITraining2U · The Agentic Operating Model
The Mandate
13 / 13
What the board must decide
Stop buying copilots. Start designing the operating model that runs while you sleep.
The technology is no longer the constraint. The constraint is whether the executive team is willing to redefine its own job — from asking the questions to ratifying the answers a team of agents has already prepared.
01
Pick one P&L line, not a platform
Start with the agent that owns your biggest tension — new-business, delivery throughput, creative allocation, studio tools, or account portfolio. 90-day pilot, one office.
02
Hire to the discipline, not the job title
Slide 6 is your org chart. Each row is an existing business discipline you already partly run somewhere — fragmented and monthly. Digitise it and put it on a daily cadence.
03
Instrument the decision, not the model
Track modelled vs. actual RM-impact on every approved decision. That single metric is the only one that matters in year one.
04
Re-write the executive job description
Move the C-suite calendar from "status meetings" to "decision reviews." The agents give back the time. Spend it on the bets only humans should make.
End · Slide 13 · The Agentic Operating Model · Malaysia EditionAITraining2U · aitraining2u.com · hi@aitraining2u.com
←→ Slides↑↓ SlidesF Fullscreen
Build It Yourself
Spin up your Marketing & Creative Agency agentic operating model.
Run these prompts in order in Claude (or ChatGPT, Lovable, Bolt, v0, Claude Code, n8n). They are pre-filled with the Marketing & Creative Agency archetype, the 5 specialist agents from this case study, and example decisions — paste them into a session and you will get a working scaffold for a multi-agent dashboard and control panel tailored to your business. Iterate from there.
Synthetic data shaped exactly like what the agents would read in production. Use it to scaffold Step 4 (Daily Decision List schema), all 5 prompts in the Knowledge Graph section, or to seed a local Obsidian vault — no production access required.
You are designing a multi-agent operating system for a Malaysian Marketing & Creative Agency business.
Archetype: A mid-sized integrated marketing agency in Malaysia — ~80 active accounts, ~120 staff (creatives, strategists, account managers), KL/PJ + Penang offices, ~RM 40M annual revenue.
Build a team of 5 specialist sub-agents + 1 master orchestrator named Chief of Staff:
- Pitch Agent — New-Business & Pitch Agent: The Pitch Strategist
- Delivery Agent — Delivery & Project Management Agent: The Project Throughput Manager
- Creative Agent — Creative & Workforce Agent: The Talent Allocator
- Studio Tech Agent — Tech & Studio Tools Agent: The Production Stack Manager
- Account Agent — Account Portfolio Performance Agent: The Client-Network Strategist
- Chief of Staff — Chief of Staff: synthesises the 5 specialists' outputs into a ranked Daily Decision List for the CEO every morning.
The team holds these 5 operational tensions simultaneously: Margin vs Utilisation, Quality vs Speed, Senior vs Junior mix, New-business vs Account-care, Scale vs Selectivity.
For each of the 6 agents, write a system prompt that includes: persona, decision authority (what it can recommend vs. approve), data it reads, tools it can call, output schema, and how it talks to the next agent in the cascade. Output 6 system prompts in markdown.
Step 2 of 6
Data inputs, memory layer, and compliance
I need to map the data inputs and memory for the Marketing & Creative Agency multi-agent system you just designed (agents: Pitch Agent, Delivery Agent, Creative Agent, Studio Tech Agent, Account Agent, Chief of Staff).
Real-time signals available in this industry: CRM (HubSpot/Salesforce), project management (Asana/Jira/Float), time tracking (Harvest/Timely), DAM (Frontify/Bynder), Adobe Creative Cloud + Figma activity, Slack/Teams signals, P&L by account (Xero), competitor RFP intel.
Regulatory and compliance feeds we must honour: PDPA (Personal Data Protection Act), SSM, LHDN, Communications and Multimedia Act.
For each of the 5 specialist agents, output a YAML schema that lists:
- data_sources: with source name, refresh cadence, access method (API / message bus / file drop), authentication style
- shared_memory_writes: what this agent commits back to the unified data + memory layer (decisions, forecasts, outcomes, learned context)
- shared_memory_reads: what it reads from the other agents' write-backs
- pii_or_compliance_flags: which fields require PDPA/regulator-specific handling
Also output a Chief of Staff section that defines the shared "Long-term Memory" (decision history, forecast vs. actual, approvals, playbook lift), the shared "Learned Context" (CEO risk appetite, peer cohort definitions, policy rules, tier-action playbooks), and the read/write rails between the specialists and the master.
Step 3 of 6
Inter-agent cascade and nightly retraining
Design the inter-agent communication cascade for the Marketing & Creative Agency multi-agent system (agents: Pitch Agent, Delivery Agent, Creative Agent, Studio Tech Agent, Account Agent, master: Chief of Staff).
Daily flow: Pitch Agent → Account Agent → Delivery Agent → Creative Agent → Studio Tech Agent → Account Agent → Chief of Staff, then Chief of Staff emits a Daily Decision List. Add one nightly feedback loop where Chief of Staff writes back to all 5 specialists with the outcomes of yesterday's approved decisions so they retrain their priors.
Deliverables:
1) A JSON message envelope schema for inter-agent messages (fields: sender, recipients, intent, payload, refs_to_data, decision_authority_request, expected_action).
2) Six worked-example messages — one for each link in the cascade — written in plain English for a Malaysian Marketing & Creative Agency context. Reference real signals (monsoon, festive windows, BNM/MCMC/MoH/JAKIM/JPJ/DOSH where relevant) so a CEO would find them credible.
3) The schema for the Chief of Staff's nightly retraining message back to each agent.
Step 4 of 6
Daily Decision List output schema
Build the Daily Decision List output schema for the Chief of Staff orchestrator in the Marketing & Creative Agency multi-agent system. This is the single artefact the CEO opens every morning.
Each list entry has:
- priority: one of P0 (immediate), P1 (this week), P2 (this month), Esc (escalate to CEO)
- decision: one-sentence description
- agents_involved: list of agent codes from Pitch Agent, Delivery Agent, Creative Agent, Studio Tech Agent, Account Agent
- rm_impact: signed number in RM (millions or thousands), positive for upside / negative for risk if unresolved
- why: one-line rationale tying the recommendation to the signals it came from
- recommended_action: one of approve / defer-24h / escalate
- proof_links: pointers to the data the agents consulted
Pre-fill 5 example entries from this case study: (1) Shift RM 80k Q3 paid-ads budget from Google Search to LinkedIn ABM, +RM 320k pipeline; (2) Reallocate 6 senior creatives from saturated retail accounts to the new BFSI pitch, +RM 540k; (3) Promote 2 mid-tier accounts to a dedicated 4-person pod, +RM 1.2M ARR expansion; (4) Switch 3 SaaS subscriptions to consolidated procurement, +RM 180k/yr; (5) Escalate 2 account leads showing burnout, RM 12M pipeline at risk.
Also output the portfolio tier snapshot the CEO sees above the list: ~12 over-performing accounts (top decile margin), ~60 on-target, ~8 under-performing (bottom decile) (over-performing / on-target / under-performing accounts).
Step 5 of 6
Executive dashboard (Next.js + Tailwind)
Build a working executive dashboard for the Marketing & Creative Agency Daily Decision List from Step 4. Use Next.js (App Router) + Tailwind + shadcn/ui. The user is the CEO.
Top of the page: portfolio tier snapshot card showing the accounts over / on / under count and the 24-hr P&L tally.
Below: the ranked Daily Decision List. Each card shows priority pill, decision, agents involved, RM-impact, one-line why, and three buttons:
- Approve (logs the approval, writes back to Chief of Staff, dispatches downstream actions)
- Defer 24h (snoozes; agent re-evaluates next cycle)
- Escalate (opens a thread to the CEO's chief of staff)
Right rail: agent activity feed showing which of the 5 specialists (Pitch Agent, Delivery Agent, Creative Agent, Studio Tech Agent, Account Agent) surfaced what overnight.
Mobile-responsive. Use Plus Jakarta Sans. Use a pink → orange brand gradient on primary actions. Output the full file tree and the code for: app/page.tsx, components/DecisionCard.tsx, components/TierSnapshot.tsx, lib/types.ts.
Step 6 of 6
Tools, actions, and approvals
Specify the tools and downstream actions each specialist agent in my Marketing & Creative Agency system can call. The agents are: Pitch Agent, Delivery Agent, Creative Agent, Studio Tech Agent, Account Agent, plus Chief of Staff. Industry-relevant integrations: HubSpot/Salesforce REST APIs, Asana/Jira webhooks, Harvest API, Adobe DAM API, Slack API, Xero API, LinkedIn Sales Navigator, generic web-research tool.
For each agent, output an MCP-style tool registry in JSON, listing tools as:
- name
- description (1 line)
- input_schema (JSON schema)
- side_effects (read-only / advisory-write / commit-write / external-action)
- approval_required_from: one of "self" / "human" / "CEO"
Also define a router contract for Chief of Staff: which agent owns which decision class, what triggers escalation to a human, and how the agent learns from approve / defer / escalate outcomes. Output as a markdown spec ready to paste into a Claude project knowledge base or n8n workflow description.
Knowledge Graph & Memory
Backbone for your Marketing & Creative Agency agentic system.
After Step 6 of the previous section you have agents and a dashboard. This section sets up the shared memory layer the agents read and write to every cycle — an Obsidian vault for human-readable notes and a Pinecone vector index for fast semantic retrieval. Every approved decision teaches the system how to be smarter tomorrow. The 5 prompts below are pre-filled for a Malaysian Marketing & Creative Agency.
The shape: Obsidian markdown vault (human-readable, version-controlled in git) ↔ Embedding pipeline (Voyage 3 large or text-embedding-3-large, 512-token chunks) ↔ Pinecone hybrid index (one namespace per agent) ↔ MCP server (the agents’ only API into memory) ↔ Nightly outcome sync that stamps results back onto the original notes so the system learns. The result is a living knowledge graph that the agents and the executive share, in one place, version-controlled, fully auditable.
Step 1 of 5
Obsidian vault structure
You are setting up an Obsidian vault to act as the human-facing knowledge layer for a Malaysian Marketing & Creative Agency multi-agent operating system. The 5 specialist agents are Pitch Agent, Delivery Agent, Creative Agent, Studio Tech Agent, Account Agent; the orchestrator is Chief of Staff. The CEO reads from this vault every morning.
Generate the vault structure:
- Folder hierarchy: /agents (one folder per agent code), /decisions, /playbooks, /learned-context, /regulatory, /operations, and the Marketing & Creative Agency-specific entity folders for: accounts, pitches, creatives, briefs, campaigns.
- Frontmatter schemas (YAML) for each note type: decision, playbook, agent-writeback, entity-profile, regulatory-change, outcome.
- Tag taxonomy: priority::P0/P1/P2/Esc, agent::(Pitch Agent, Delivery Agent, Creative Agent, Studio Tech Agent, Account Agent), status::approved/deferred/escalated, outcome::win/loss/pending, regulator::(PDPA / SSM / LHDN).
- Dataview queries the CEO uses at 06:00 daily: (a) today's Decision List, (b) this week's escalations, (c) agent-by-agent RM-impact tally, (d) decisions whose outcome::pending is more than 7 days old.
Output a clear directory tree + one fully written sample note per note type (6 notes) with realistic Marketing & Creative Agency-flavoured content.
Step 2 of 5
Pinecone vector index schema
Design the Pinecone vector index that backs the agents' shared memory for the Marketing & Creative Agency system from the previous prompts. The agents are Pitch Agent, Delivery Agent, Creative Agent, Studio Tech Agent, Account Agent (plus Chief of Staff orchestrator). Scale: mid-sized agency (~120 staff).
Requirements:
- One Pinecone namespace per agent (Pitch Agent, Delivery Agent, Creative Agent, Studio Tech Agent, Account Agent) plus a shared 'orchestrator' namespace and a 'regulatory' namespace.
- Vector dimensions: 3072 (Voyage 3 large or OpenAI text-embedding-3-large). Justify whether to downscale to 1024 (matryoshka) for cost.
- Per-vector metadata fields: doc_id, agent (one of Pitch Agent, Delivery Agent, Creative Agent, Studio Tech Agent, Account Agent | Chief of Staff | regulatory), entity_type (one of accounts, pitches, creatives, briefs, campaigns), entity_id, ts (unix), decision_class (forecast | decision | playbook | outcome | regulatory-change), priority, rm_impact (signed float), outcome (win | loss | pending), tags (array).
- Hybrid retrieval: sparse + dense. Include BM25 for exact lookup of entity IDs (Marketing & Creative Agency-specific codes — e.g., branch IDs, project codes, SKU codes, tower IDs).
- Settings: serverless vs pod-based — recommend one for mid-sized agency (~120 staff). Justify replica count and metadata-index choice.
- Chunking: 512-token windows, 64-token overlap, but also one vector per H2/H3 section so the agents can cite a specific section back to the CEO.
Output as: (a) a Terraform module that provisions the index, (b) a Python pinecone-client setup script that creates the namespaces, (c) a JSON schema validator for the metadata fields, ready to enforce on every upsert.
Step 3 of 5
Embedding & ingestion pipeline
Build the embedding and ingestion pipeline that turns Obsidian markdown notes into Pinecone vectors for the Marketing & Creative Agency agents (Pitch Agent, Delivery Agent, Creative Agent, Studio Tech Agent, Account Agent, Chief of Staff).
Pipeline:
1. File watcher on the vault folder (chokidar in Node or watchdog in Python) that fires on save and on git-pull.
2. Parse YAML frontmatter and markdown body via python-frontmatter or gray-matter.
3. Chunk body by H2/H3 boundaries AND by ~512-token windows with 64-token overlap. Preserve heading path as chunk.section_path metadata.
4. Embed via Voyage 3 large (or text-embedding-3-large) — async, batched at 100 items, retry with exponential backoff on 429/5xx.
5. Extract metadata: agent_namespace (from path /agents/<code>/...), entity links (parse [[wikilinks]] from body and map to entity_id), decision_class (from frontmatter), regulatory mentions (regex hit on PDPA / SSM / LHDN), rm_impact (from frontmatter), outcome (from frontmatter).
6. Upsert with deterministic IDs: sha256(file_path + chunk_index). Delete-then-upsert on save to avoid stale chunks.
7. Write back to the note's frontmatter: pinecone_ids[], last_embedded_at, chunk_count. Commit back to git for auditability.
Deliverables:
- A Python repo (FastAPI + Pinecone client + Voyage client + python-frontmatter + watchdog) with a docker-compose.yml that starts the watcher and a small admin UI for the CEO's chief of staff to trigger re-embed on stale notes.
- An on-failure alerter that posts to Slack/Telegram if any sub-step errors out twice in a row.
Step 4 of 5
MCP server for agent queries
Build an MCP (Model Context Protocol) server that exposes the Marketing & Creative Agency Obsidian vault + Pinecone index as queryable tools for the agents (Pitch Agent, Delivery Agent, Creative Agent, Studio Tech Agent, Account Agent) and Chief of Staff.
Expose these tools:
- search_memory(query, agent_namespace?, entity_type?, entity_id?, date_range?, top_k=10) — hybrid Pinecone retrieval; returns chunks with source-note paths, section_path, and frontmatter metadata.
- get_decision_history(entity_id, days=30) — returns every Daily Decision List entry that touched this accounts or other entity.
- get_playbook(name) — returns the most up-to-date playbook for a named scenario. Known playbooks for this industry include: pitch-win playbook, account-rescue playbook, freelancer-onboarding playbook.
- get_agent_writeback(agent_code, since) — returns recent forecasts/decisions written by one agent.
- get_outcome(decision_id) — returns the actual outcome of an approved decision; used by the nightly Chief of Staff retraining loop.
- write_note(path, frontmatter, body) — writes a new markdown note to the vault (triggers re-embedding via the Step-3 watcher).
- propose_playbook_update(playbook_name, diff, evidence_decision_ids) — drafts a markdown PR to the playbook with linked evidence.
Implement as a TypeScript MCP server using @modelcontextprotocol/sdk. Configure manifests for: (a) Claude Desktop, (b) Claude Code, (c) n8n's MCP node so the agents can call it as part of the cascade. Output the full source code + a 1-page how-to-install.
Step 5 of 5
Nightly knowledge-graph sync
Wire up the nightly knowledge-graph sync from production operations to the Obsidian vault and Pinecone for the Marketing & Creative Agency multi-agent system (Pitch Agent, Delivery Agent, Creative Agent, Studio Tech Agent, Account Agent, Chief of Staff).
Each night at 02:00 MYT:
1. Pull yesterday's outcomes from production: for each Daily Decision List entry, fetch what the CEO actually approved / deferred / escalated, the realised RM-impact, and any downstream effect.
2. Stamp outcomes onto each decision note's frontmatter (outcome::win | loss | pending, actual_rm_impact, time_to_outcome).
3. Generate a 'daily writeback' note per agent (Pitch Agent, Delivery Agent, Creative Agent, Studio Tech Agent, Account Agent) summarising what it saw, what it recommended, what was approved, and the delta vs forecast. Save under /agents/<code>/writeback/YYYY-MM-DD.md.
4. For every new or updated note, the Step-3 pipeline re-embeds and upserts to Pinecone.
5. Run a "lessons learned" extractor: prompt the Chief of Staff model with the day's outcomes and ask for 3-5 playbook updates. Append as drafts to /playbooks/_drafts/ for CEO review.
6. Push a Slack/Telegram digest to the CEO's chief of staff: the top 5 lessons, plus any decisions stuck at outcome::pending for >7 days.
Deliverables: (a) an n8n workflow JSON, (b) a Claude Code skill, or (c) a Python cron job — pick the best fit for a mid-sized agency (~120 staff) and explain why. Include error handling, retry, and a fail-open mode that alerts a human on any failure.
