Manila Before Manhattan
Building Agentic Finance in Frontier Markets
We believe agentic finance leapfrogs fastest where traditional financial infrastructure is weakest. The world saw this with mobile phones leapfrogging landlines, and neobanks and digital wallets leapfrogging traditional banks.
Brittle infrastructure creates an opportunity for agents to have a larger surface area for impact. They create infrastructure that never existed, rather than simply optimize existing financial workflows.
Definitionally, we refer to agentic finance as the ability of enterprises to delegate financial decisions and actions to autonomous agents, within defined authority and constraints.
This spans the full financial lifecycle: before a transaction (contracting, invoicing, underwriting), during it (authorization, payment, clearing, settlement), and after it (reconciliation, accounting, tax), as well as broader functions such as treasury and financial planning.
Agentic payments is a subset of this capability focused on the initiation and movement of money. Agentic finance is distinct from, though it can overlap with, agentic commerce, which generally refers to agents discovering, evaluating, and purchasing on behalf of a buyer.
We think the adoption path runs through emerging markets first, and hence the opportunity to build large and valuable platforms.
In mature markets with established banking infrastructure, agentic finance is an optimization layer for existing banks and fintechs. It makes existing workflows faster and more efficient. This is interesting, but incremental. And the benefits accrue to incumbents with existing distribution.
In emerging markets, where most businesses have minimal finance teams, where cross-border settlement takes days and costs 3-5%, where a single CPA serves dozens of businesses because there aren't enough to go around; agentic finance is first contact with first world infrastructure.
These markets are building new payment rails, writing new regulation, and reforming their financial systems while AI eats software around the world. They have less legacy architecture to retrofit and stronger incentives to experiment. The same leapfrogging logic that brought mobile money to Kenya before Kansas will likely bring agentic finance to Manila before Manhattan.
There are two forces that will drive how fast this will happen: AI’s scaling laws and the political-economy around its adoption.
Scaling laws determine the frontier. Better models, more compute, more data, and lower inference costs will continue to expand what agents can do.
We think political economy is rapidly the binding constraint, because it determines what agents are allowed to do.
Finance does not run on technology alone. It runs on identity systems, licenses, payment rails, data privacy rules, AML/BSA compliance, liability regimes, the incentives of incumbent institutions, and increasingly the attitudes of decision-makers and society towards AI. An agent may become technically capable of underwriting credit, moving money, or managing a company's treasury long before the surrounding financial system permits it to do so autonomously. This is what we mean by political economy as the constraint.
Most writing on agentic finance focuses on how intelligent the agents will become. We think an equally important question is what the financial system will let them build.
For example, agents that interact with money have to operate alongside a heavily regulated sector in every economy on earth: banking. The reason is because if we believe agents will birth new economic systems, then they’ll have to play a crucial role to intermediate the demand and supply of credit. The world faces a $5.7 trillion credit gap - $2.5 trillion for global trade finance alone.
Solving this means not just integrating with the banking and credit infrastructure of one country, but building a network where agents can be interoperable across multiple jurisdictions.
One approach for builders is to gain clarity on the agentic finance products they’re pursuing, as these would have different levels of regulatory and compliance complexity.
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A Framework for Products
We think about this as a 2x2 with two relevant dimensions: single player vs multiplayer and domestic vs cross-border.
- Single player: One enterprise automates its own finances; counterparties can receive ordinary invoices, reminders, or payments.
- Multiplayer: Multiple enterprises, subsidiaries, or their agents coordinate financial decisions, terms, funding, or payment execution.
- Domestic rails: Payments and financial workflows operate within one country’s financial system.
- Cross-border rails: Payments or financial workflows connect parties across national borders, coordinating across financial systems and jurisdictions. Multiple currencies alone don’t make a workflow cross-border.
| Domestic | Cross-border | |
|---|---|---|
| Single player | Tax Compliance Agent: Classifies transactions and prepares tax filings. Invoice Agent: Issues reminders and manages domestic receivables for one business. Autonomous A/P: Processes approvals and pays local suppliers, even when several internal agents participate. Accounting Reconciliation Agent: Matches domestic bank activity to the general ledger, classifies entries based on local accounting standards | Invoice Agent: Collections workflow applied to international receivables Autonomous A/P: Handles international payment requirements for the same business. Accounting Reconciliation Agent: Reconciles international wires and associated fees. |
| Multi player | Cross-Company Supply Chain Agent: Buyer, supplier, and bank coordinate payment terms, early payment, and financing. Domestic placement assumes buyer and supplier are in the same country. Invoice Agent: financing extension: Coordinates with an external lending or collections agent when credit is involved in the transaction Vendor Negotiation Agents: Negotiate commercial terms with independent suppliers. | Financial World Model: Coordinates financial planning and capital allocation across subsidiaries, business units, and countries, balancing local needs with group objectives. Global Procure-to-Pay Agents: Explicitly coordinates procurement, suppliers, logistics, escrow, and settlement across multiple countries. Cross-Company Supply Chain Agent - international version: The same buyer–supplier–bank coordination across borders. Diaspora Remittance Agent: Coordinates overseas funding and domestic distribution across sender, recipient, and billers. |
The space is moving so fast that we’re certain there are other non-obvious use cases waiting to be discovered. One interesting observation is that overseas diaspora remittances are an existing multi-agent payment flow hiding in plain sight.
For example, $40 billion flows annually into the Philippines from overseas workers. The payments flow is this: Sender earns abroad → sends via remittance app → family receives in local wallet → pays bills/tuition/rent/groceries/other expenses → sends confirmation back to the sender that funds have been used → asks for more the following month.
This is a multi-hop, multi-party automated flow with terrible economics (3-5% fees, 1-2 day settlement). An agent-native remittance rail that can settle via stablecoins and auto-distribute to agents of many billers on the receiving end sounds like an obvious product.
Here’s each of these use cases in more detail:
Tax Compliance Agent
Ana, owner of a 15-person logistics company in Cebu
Problem: Ana pays an accountant ₱30,000/month to handle BIR tax compliance: preparing 2307 withholding certificates, filing quarterly returns, reconciling VAT input and output across her PHP and USD accounts. The accountant makes errors that Ana only discovers when she gets an assessment notice.
Agent Solution: An agent monitors all transactions, auto-classifies them by BIR tax category, generates 2307 forms for each vendor payment, and prepares quarterly filings in the format BIR's eFPS system expects. It flags transactions it can't confidently classify and routes them to Ana with a one-tap approve/edit interface. At month-end, it reconciles her peso and dollar accounts across two banks and produces a single consolidated view.
Impact: Ana's compliance costs drop from ₱30,000/month to near zero. She files on time every quarter without hiring a full-time accountant she can't afford — and that the market can't supply.
Autonomous A/R: Invoice Agents
Tomoko, freelance design studio owner with 8 active clients
Problem: Tomoko sends invoices through her neobank but has no systematic follow-up. She realizes invoices are overdue only when cash gets tight, then awkwardly emails clients one at a time.
Agent Solution: An agent monitors all outstanding invoices. At 7 days past due, it drafts a reminder email. At 14 days, the tone firms up. At 30 days, it flags the invoice for Tomoko with a recommended action. Tomoko approves templates once; the agent handles the rest. Past 30 days, the agent autonomously offers a collections notice, working with a loan service agent maintained by another startup, or offer invoice financing to help smoothen the cash gap.
Impact: Average days-to-payment drops from 45 to 19\. Tomoko never has to write an awkward collections email.
Accounting Reconciliation Agent
Raj, startup controller managing a QuickBooks integration
Problem: Every month-end, Raj spends two days reconciling bank transactions with QuickBooks. The sync misses edge cases: refunds coded wrong, transfers between accounts double-counted, international wire fees categorized inconsistently.
Agent Solution: An agent runs nightly, pulling all new transactions and comparing them against QuickBooks entries. It identifies mismatches, suggests corrections, and auto-fixes known patterns (e.g., always categorize bank wire fees as 'Bank Service Charges'). Raj gets a morning report of what was auto-fixed and what needs his judgment.
Result: Month-end close drops from 3 days to 4 hours. The error rate on categorization falls to near zero.
Autonomous A/P
No human operator — a fully autonomous AP agent
Problem: A 200-person company processes 400 bills/month across 3 approval levels and 80 vendors. The Accounts Payable team needs to scale alongside the business, but resources are tied up to compute and AI engineers. We've talked to BPO finance teams in Makati who spend 2 to 3 full-time headcount on exactly this workflow.
Agent Solution: An agent network handles the entire AP lifecycle: The Bill Agent receives and parses invoices from email, matches to POs from the procurement system, and codes to GL accounts. The Approval Agent routes to the right approver chain based on amount and department, follows up on stale approvals, and escalates blockers. Approvers (like function or business unit heads) are represented by Policy Agents with built-in fiduciary and governance skills to make approve / decline decisions on behalf of their principals. The Payment Agent queues approved bills for payment on optimal dates, handles international wire requirements for overseas vendors, and archives payment confirmations. Compliance Agent ensures every payment has proper documentation, flags unusual vendor banking changes, and maintains the approved vendor list.
Impact: The company runs AP with zero full-time staff. The finance lead reviews a daily summary dashboard. Exception rate: under 1%.
Vendor Negotiation Agents
Javier, Operations Manager at a scaling startup
Problem: The company's SaaS spend has grown 3x in a year but nobody has renegotiated any vendor contracts. They're paying list price on tools they've outgrown and still paying for tools nobody uses.
Agent Solution: An agent analyzes 12 months card transactions by vendor: total spend, usage trends, per-seat costs, and contract renewal dates. It identifies the top 10 renegotiation opportunities by potential savings, drafts outreach emails to each vendor with specific asks (volume discount, annual prepay discount, seat reduction), and tracks responses. It knows common discount thresholds for major SaaS tools.
Impact: The company saves $180K/year in SaaS spend through systematic renegotiation it never would have done manually.
Cross-Company Supply Chain Agent
A startup and its key supplier, using an AI-native bank
Problem: The startup needs 60-day payment terms but the supplier needs cash within 15 days. Today this mismatch causes friction, manual negotiations, and sometimes lost suppliers.
Agent Solution: A supply chain finance agent negotiates directly with the supplier's agent. The bank sees both sides of the relationship. Thus, it offers dynamic discounting: the supplier can get paid early at a small discount, funded by the bank’s supply chain finance product. The agents negotiate the optimal discount rate based on both parties' cash positions, agree on terms, and execute the payment — all without human intervention beyond initial policy setting.
Impact: Supply chain financing happens automatically between trusted parties, eliminating billions in market inefficiency.
Global Procure-to-Pay Agents
Actors: a manufacturer’s procurement agent, supplier agents, logistics agents, agentic bank accounts on all sides
Problem: A vehicle manufacturer orders components from 50 suppliers across 5 countries. Purchase orders, invoices, shipping confirmations, quality inspections, and payments are all separate manual workflows involving 20 different humans.
Agent Solution: The manufacturer’s procurement agent issues a machine-readable RFQ via MPP. Supplier agents respond with pricing, lead times, and quality certifications. The procurement agent selects winners, creates purchase orders as smart contracts. An escrow agent facilitates the contracts. When the supplier’s logistics agent confirms shipment (verified via a shipping API), partial payment releases. When the startup’s receiving agent confirms quality inspection, final payment releases from the escrow agent. All cross-border payments flow through agent-native operating accounts with automatic FX optimization.
Payment Mechanic: International wires and/or stablecoin payments triggered by agent-verified milestones. Escrow accounts per PO. FX locked at commitment, settled at delivery.
Impact: The entire procure-to-pay cycle runs without humans. A 50-supplier, 5-country supply chain settles autonomously.
The Financial World Model
An Asian conglomerate’s executive runs its annual planning process across 50+ subsidiaries
Problem: Strategic decisions (hire 2000 people? open a new market? acquire a competitor?) require weeks of financial modeling that's outdated the moment it's complete.
Agent Solution: A financial digital twin operates as a world model: it maintains a real-time simulation of the conglomerate's finances on an entity and business-unit level, fed by bank data, payroll, CRM, and operational metrics, along with macroeconomic data from government sources. It's complemented by the company’s tribal knowledge, connected through a conglomerate-spanning knowledge graph. Executives can ask natural-language questions: 'What happens if we hire 500 engineers in Q3?' 'Can we afford to acquire Company X at $5B?' 'What's our break-even date if we lose our biggest customer?' The twin runs Monte Carlo simulations in seconds, showing probability distributions of outcomes rather than single-point estimates.
Impact: Strategic financial decisions backed by real-time simulation instead of stale spreadsheets. Decision latency drops from weeks to minutes.