Moving the Malaysian retail & SME bank from human-prompted AI assistants to a coordinated team of agents that runs every branch, customer cohort, and balance-sheet line on a daily schedule — and delivers a ranked decision list to the CEO every morning.
Prepared forBoard & C-Suite
FormatOnline Reference
Case StudyNational Retail & SME Bank · Peninsular Malaysia
Your AI investment will not pay back until the AI stops waiting to be asked.
Most Malaysian banks still treat AI as a faster search bar — a tool that produces value only when a banker pulls it. The next operating model inverts that: a team of specialist agents consumes the bank's data on a fixed daily schedule, weighs the trade-offs (BNM rate moves, deposit-flow volatility, branch-traffic shifts, AML alert backlogs, capital and liquidity constraints), and delivers a ranked, ready-to-approve Decision List to the CEO every morning.
The ShiftFrom banker 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 store, SKU, shift, asset, and tier — 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 — Malaysian retail & SME bank
A Malaysian bank where rates, deposits, branch traffic, and AML alerts all move every hour. The ideal stress-test.
Retail and SME banking in Malaysia compresses every operational discipline of a large enterprise into a single business day: loan and deposit pricing against BNM rate moves, branch footfall shifts as customers migrate to digital, AML alert triage under AOB and AMLA scrutiny, branch and contact-centre workforce governed by IBG agreements and the Employment Act, capital and liquidity buffers under Basel III. Multiplied across hundreds of touchpoints — from Klang Valley flagships to East Malaysia agent banks — no human team can hold the full state of the bank in working memory.
National
Branch + agent-bank + digital footprint across Malaysia
BNM-regulated
Capital, liquidity, AML, conduct under BNM & AOB oversight
Digital channels always-on · cross-border FX · payment-rail SLAs
The five operational tensions the team of agents must hold simultaneously
Tension 1
NIM vs. Volume
Price for margin and lose share, or price for share and erode the spread?
Tension 2
Liquidity vs. Earnings
Hold the buffer against a deposit shock, or deploy into higher-yield assets?
Tension 3
Branch vs. Digital
Keep the costly branch network, or push digital and risk losing the SME relationship?
Tension 4
Compliance vs. Friction
Tighten the AML / KYC screen, or eat the false positives and the abandonment?
Tension 5
Scale vs. Consolidate
Open new flagships, or rationalise overlapping sub-scale branches?
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 branch'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.
Pricing Agent
Risk & Pricing
The Pricing & Credit Risk Strategist
Watches: BNM rate signals, FD & loan book rates, credit-scoring drift, deposit-pricing competition, cross-sell propensity, NIM by segment.
Decides: the right loan and deposit pricing across segments, and the 14-day demand forecast everyone else plans against.
Decides: the 14-day branch and contact-centre roster — compliant, traffic-matched, with attrition risk flagged early.
Branch traffic planningIBG compliance
Risk Agent
Operational Risk
The Process Reliability & AML Manager
Watches: AML / CFT alert flow, fraud signals, core-banking and payment-rail uptime, vendor risk, KRI thresholds, conduct & complaints data.
Decides: which AML alerts to escalate, which KRIs to flag, when to throttle a vendor — auto-prioritises operational-risk actions to the right desk.
AML triageAnomaly detection
Network Agent
Branch Portfolio Performance
The Network Strategist
Watches: per-branch P&L, cost-to-income, deposit + loan books, customer NPS, catchment demography, digital-cannibalisation signals, and what the other agents report.
Decides: classifies every branch as Overperform / On-Target / Underperform vs. its peer cohort, and triggers the right tier action.
Branch cohort analyticsConsolidation diligence
Chief of Staff
Chief of Staff
The Synthesis Layer
Watches: what all five specialists are recommending, plus the bank P&L, capital ratios, liquidity ratios, and key BNM-reportable metrics.
Decides: reconciles conflicts (e.g., reprice for NIM vs. defend deposit share), ranks the day's calls by expected RM-impact, and presents the shortlist to the 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.
Peer cohort matchingGroups branches by catchment demography, format, sub-segment mix — apples to apples.
Composite performance scoringCost-to-income, deposit book, loan book, NPS, digital-cannibalisation index — rolled into one score.
Tier classificationOverperform · On-Target · Underperform vs. true peers.
Intervention uplift testingOnly triggers playbooks (format upgrade, advisory specialisation) that have moved similar branches before.
Consolidation & replication diligenceSurfaces the evidence pack for closing, merging, or replicating a branch.
Branch-network strategist · bank-economics analyst · analytics lead with causal background.
"Of every branch, here are the Overperformers to replicate, the Underperformers to fix or consolidate, and the specific action proven to work on branches like these."
Conflict reconciliationWhen Pricing Agent wants to reprice for NIM but Network Agent flags branch-share risk — adjudicates.
Risk-appetite calibrationLearns the 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 and BNM-risk, 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 — Pricing Agent's pricing forecast is the input to Treasury Agent, Branch Agent, and Network Agent. The Orchestrator consumes all five and emits one artefact: a ranked decision list for the executive.
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.
FromPricing Agent · Pricing Strategist
→tells
ToTreasury Agent · Treasury Planner
"BNM is likely to raise the OPR at this week's MPC. Fund the duration gap before the announcement, not after. If we wait, the funding cost climbs and the NIM gain on the new rate gets wiped out."
FromPricing Agent · Pricing Strategist
→tells
ToBranch Agent · Branch Planner
"FD-ladder maturities cluster next Friday — expect a wave of high-value customers walking into the flagship branches to reprice or roll over. Pre-roster wealth advisors; don't staff to last week's footfall."
FromRisk Agent · Op Risk Manager
→tells
ToPricing Agent · Pricing Strategist
"Don't onboard this SME segment without tighter screening — the cluster has crossed AML alert thresholds and the false-positive cost is climbing. Either we tighten KYC for this segment, or the line of business gets escalated to BNM."
FromAll four specialists
→feed
ToNetwork Agent · Network Strategist
"Here is each branch's performance against its true peer group — same catchment demography, same sub-segment mix, same format. Three buckets: Overperform, On-Target, Underperform. Each bucket gets a specific playbook (format upgrade, advisory specialisation, agent-bank conversion) that has been validated on similar branches before."
FromAll agents
→feed
ToChief of Staff · Chief of Staff
"Here is every recommendation on the table today. Rank them by expected RM-impact, discounted for BNM and conduct risk according to the CEO's appetite. Surface only the top 3–5. Everything else routes to the line-of-business head."
FromChief of Staff · Chief of Staff
↺loops to
ToAll agents
"The CEO approved 4 of 5 decisions yesterday. Here is what actually happened in deposits, loans, AML, and branch traffic. Every agent: re-score your forecasts against the outcome. The CEO's revealed risk appetite has shifted — 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 (core banking, treasury, branch ops, AML platform, payment rails).
23:59 · Outcome data flows back as ground truth. Deposit flows, NIM, AML SLAs and tier moves are scored.
Why It Compounds
The reactive copilot has no memory of yesterday's bet
Forecast scoring — every deposit-flow and pricing prediction is measured against actuals. Drift is detected and the agent self-corrects.
Risk-appetite learning — every CEO approval teaches the Orchestrator how aggressive the leadership really is, not what the BCM policy doc says.
Playbook validation — Network Agent only triggers a tier action when matched-branch evidence says it has worked before. Each triggered action retrains the evidence base.
Compounding edge — Year 1 you replace the daily ALCO huddle and weekly branch-ops review. Year 2 the system out-forecasts the 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 pricing, liquidity, branch workforce, operational risk, and branch portfolio tier. The CEO judges the trade-off — the answer is already assembled.
Network Agent · Branch tier snapshot · every branch benchmarked against its peer cohort today
OverperformTop decile
Flagshipbranches
Trigger: replicate the Mid Valley flagship's advisory + leverage playbook across cohort-matched branches — meaningful per-branch quarterly uplift modelled.
On-TargetMid pack
Mostbranches
Trigger: maintain and tune. A small group approaching the upper band — pre-qualified for the Overperform playbook next cycle.
UnderperformBottom decile
At-riskbranches
Trigger: intervention plans (format reset, agent-bank conversion) and consolidation reviews where an overlapping branch cluster has underperformed for 12 consecutive months.
#
Recommended decision
Modelled impact
Source agents
Action
1
Reprice the FD ladder ahead of the BNM MPC announcement & pre-fund the duration gap
Pricing Agent reads a high probability of an OPR hike on Thursday. Treasury Agent confirms the duration gap widens materially without a reprice. Move FD board rates pre-MPC; pre-fund the gap before wholesale costs climb.
7-figure NIM upsideHigh confidence
Pricing Agent · Treasury Agent
P0Approve
2
Portfolio action — consolidate the overlapping Klang Valley branch cluster · replicate the Mid Valley flagship format
Network Agent: a cluster of sub-scale Klang Valley branches is in the bottom decile of its peer cohort for 12 months running. The Mid Valley flagship format is in the top decile; cohort-matched replication has historically delivered a meaningful per-branch quarterly lift while consolidation removes overlap.
7-figure annualisedMedium-high confidence
Network Agent · Branch Agent · Treasury Agent
P0Approve
3
Add tighter AML screening overlays on a cross-border SME corridor before Friday's weekly clearing
Risk Agent flags an alert spike from SMEs onto a watch-listed corridor. Without intervention this week, the bank faces materially higher AML-penalty exposure and a probable BNM enquiry. Risk-tier the corridor; tighten KYC; brief the line of business.
7-figure avoided penaltyRisk-adjusted
Risk Agent
P0Approve
4
Pre-fund the East Malaysia ATM network ahead of the long-weekend cash demand spike
Treasury Agent forecasts a sharp lift in ATM cash demand Fri–Sun on a long-weekend pattern. Cash-in-transit can deliver if approved by 10:00 today. Avoid refusals and the NPS hit they cause.
6-figure avoided refusalHigh confidence
Treasury Agent · Branch Agent
P2Approve
5
Escalate the SME RM-team attrition signal in Penang · pipeline at risk
Branch Agent detects a cluster of senior SME relationship managers showing attrition signals at competitor banks in Penang. The stalled pipeline they carry exceeds policy thresholds. Retention conversation needs to happen this week — not a routine HR action.
7-figure downsideIf unresolved this quarter
Branch Agent · Network Agent
EscRoute
Slide 11 — The Daily Prioritised Decision List · Illustrative · MalaysiaAITraining2U · The Agentic Operating Model
The Implementation Path
12 / 13
From a single branch teller to the full operating model
Four phases. Hire as you go. Right-size for your maturity.
Banks 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 banks should start at Phase 1 — a single agent in the branch teller's tablet at one flagship.
Phase 01 · AssistTeller Co-pilotMonths 0–2
One agent in the branch teller / RM's tablet. A daily action checklist at the counter — not a dashboard, not a CRM screen.
Entry bar — your starting maturity
Core banking + CRM exporting clean data. Branch teams still run morning huddle on whiteboard / WhatsApp.
A weekly cross-agent scorecard plus same-day escalations when agents disagree (e.g., Pricing Agent wants to reprice; Risk Agent flags AML pressure).
Illustrative first project
Bank-wide quartet rollout. Cascade goes live: pricing → funding → branch roster → operational risk 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 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 + risk + branch-ops team. Executive ready for one-click approval.
Agents activated
Pricing AgentTreasury AgentBranch AgentRisk AgentNetwork 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, BNM-risk-quantified calls awaiting one-click approval.
Illustrative first project
Full bank rollout across every branch, agent bank, and digital channel. Year 2: the system out-forecasts the ALCO and branch-ops teams 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 — pricing & risk, liquidity, branch workforce, operational risk, or branch portfolio. 90-day pilot, one product line.
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 Retail / SME Bank agentic operating model.
Run these prompts in order in Claude (or ChatGPT, Lovable, Bolt, v0, Claude Code, n8n). They are pre-filled with the Retail / SME Bank 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 Retail / SME Bank business.
Archetype: A Malaysian retail and SME bank — ~280 branches, ~12M customers, multi-product (deposits, lending, cards, wealth, SME), nationwide footprint.
Build a team of 5 specialist sub-agents + 1 master orchestrator named Chief of Staff:
- Pricing Agent — Risk & Pricing Agent: The Pricing & Credit Risk Strategist
- Treasury Agent — Liquidity & Treasury Agent: The Balance-Sheet Planner
- Branch Agent — Branch & Workforce Agent: The Branch Network Planner
- Risk Agent — Operational Risk Agent: The Process Reliability Manager
- Network Agent — Branch Portfolio Performance Agent: The 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 Risk, Deposit vs Lending, Branch vs Digital, Compliance vs Speed, Scale vs Exit.
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 Retail / SME Bank multi-agent system you just designed (agents: Pricing Agent, Treasury Agent, Branch Agent, Risk Agent, Network Agent, Chief of Staff).
Real-time signals available in this industry: Core banking transactions, credit applications, deposit flows, FX, BNM rate signals, AML alerts, branch traffic, teller/RM utilisation, customer-360, regulatory feeds (BNM, PIDM).
Regulatory and compliance feeds we must honour: BNM, Bursa, AML/CFT (BNM Sectoral Guidelines), Basel III, PIDM, PDPA.
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 Retail / SME Bank multi-agent system (agents: Pricing Agent, Treasury Agent, Branch Agent, Risk Agent, Network Agent, master: Chief of Staff).
Daily flow: Pricing Agent → Treasury Agent → Branch Agent → Risk Agent → Network 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 Retail / SME Bank 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 Retail / SME Bank 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 Pricing Agent, Treasury Agent, Branch Agent, Risk Agent, Network 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) Reprice the 5-year FD ladder ahead of BNM MPC, +RM 12.4M NIM; (2) Consolidate 4 Klang Valley branches into 1 flagship, +RM 14.6M annualised; (3) Add overlay rules to AML model on cross-border SME flows, +RM 9.1M avoided penalty; (4) Pre-fund Sabah branch ATMs for long weekend, +RM 1.8M avoided refusal; (5) Escalate 3 SME RMs leaving for competitor, RM 240M pipeline at risk.
Also output the portfolio tier snapshot the CEO sees above the list: ~36 over-performing branches, ~224 on-target, ~20 under-performing (over-performing / on-target / under-performing branches).
Step 5 of 6
Executive dashboard (Next.js + Tailwind)
Build a working executive dashboard for the Retail / SME Bank 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 branches 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 (Pricing Agent, Treasury Agent, Branch Agent, Risk Agent, Network 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 Retail / SME Bank system can call. The agents are: Pricing Agent, Treasury Agent, Branch Agent, Risk Agent, Network Agent, plus Chief of Staff. Industry-relevant integrations: Core banking API (Temenos/Finacle), BNM rate feed, internal AML/KRI systems, branch staff scheduling (Workday/Kronos), CRM (Salesforce Financial Services Cloud), Bloomberg/Refinitiv.
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 Retail / SME Bank 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 Retail / SME Bank.
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 Retail / SME Bank multi-agent operating system. The 5 specialist agents are Pricing Agent, Treasury Agent, Branch Agent, Risk Agent, Network 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 Retail / SME Bank-specific entity folders for: branches, customers, products, loans, AML cases.
- Frontmatter schemas (YAML) for each note type: decision, playbook, agent-writeback, entity-profile, regulatory-change, outcome.
- Tag taxonomy: priority::P0/P1/P2/Esc, agent::(Pricing Agent, Treasury Agent, Branch Agent, Risk Agent, Network Agent), status::approved/deferred/escalated, outcome::win/loss/pending, regulator::(BNM / PIDM / AMLA / PDPA).
- 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 Retail / SME Bank-flavoured content.
Step 2 of 5
Pinecone vector index schema
Design the Pinecone vector index that backs the agents' shared memory for the Retail / SME Bank system from the previous prompts. The agents are Pricing Agent, Treasury Agent, Branch Agent, Risk Agent, Network Agent (plus Chief of Staff orchestrator). Scale: national retail bank (~280 branches, 12M customers).
Requirements:
- One Pinecone namespace per agent (Pricing Agent, Treasury Agent, Branch Agent, Risk Agent, Network 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 Pricing Agent, Treasury Agent, Branch Agent, Risk Agent, Network Agent | Chief of Staff | regulatory), entity_type (one of branches, customers, products, loans, AML cases), 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 (Retail / SME Bank-specific codes — e.g., branch IDs, project codes, SKU codes, tower IDs).
- Settings: serverless vs pod-based — recommend one for national retail bank (~280 branches, 12M customers). 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 Retail / SME Bank agents (Pricing Agent, Treasury Agent, Branch Agent, Risk Agent, Network 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 BNM / PIDM / AMLA / PDPA), 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 Retail / SME Bank Obsidian vault + Pinecone index as queryable tools for the agents (Pricing Agent, Treasury Agent, Branch Agent, Risk Agent, Network 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 branches or other entity.
- get_playbook(name) — returns the most up-to-date playbook for a named scenario. Known playbooks for this industry include: BNM rate-move playbook, branch-consolidation diligence, AML escalation 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 Retail / SME Bank multi-agent system (Pricing Agent, Treasury Agent, Branch Agent, Risk Agent, Network 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 (Pricing Agent, Treasury Agent, Branch Agent, Risk Agent, Network 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 national retail bank (~280 branches, 12M customers) and explain why. Include error handling, retry, and a fail-open mode that alerts a human on any failure.
