Sits before every agent call. Checks the agent, owner, team budget, provider, policy, approval, and receipt — in that order, every time. Govern LLM spend today. Agent-driven API and tool spend tomorrow. One control plane for both.
Agents spend money. Agentagon governs money.
Payment rails like x402, MPP, and Stripe move money for agents just fine. None of them tell you who can spend, with what budget, on which providers, or with what audit trail. As every API call becomes a paid call, that gap shows up on the bill.
Your agents are calling OpenAI, Anthropic, Stripe, x402 endpoints, vector DBs, and dozens more. Spend is scattered across vendor dashboards, on-chain transactions, and monthly invoices. You learn the bill at month-end, not in the moment.
Any agent can call any provider for any amount. No team budgets, no employee delegation, no provider allowlists, no approval thresholds. One runaway prompt and you're explaining a five-figure invoice that nobody approved.
Finance asks "who spent this?" and you have an account ID. Compliance asks for a control matrix and you have a hunch. Reconciling Stripe, x402, MPP, and ERP every quarter is a manual, error-prone fire drill.
From the LLM call to the SaaS API to the x402 endpoint, the same agents are spending across every layer. The boundaries are dissolving fast. One control plane has to govern all of it.
Most enterprise AI spend is still OpenAI and Anthropic invoices. Easy to track at the org level, hard to attribute by team, harder to cap before the bill arrives. One enterprise we work with spends $2M+ a year here alone.
Agents are calling Apollo, Stripe, Pinecone, and a long tail of paid APIs on behalf of teams. Subscriptions, API keys, and corporate cards stretched across surfaces nobody owns. Most of every subscription goes unused.
x402, MPP, and MCP-paid tools let agents pay any provider, per call, without a checkout. Volume is small today and explosive in 18 months. Every one of those calls needs the same governance as the LLM bill.
Ramp, Brex, Airbase, and Coupa understand AI spend at the invoice layer — providers, models, cards, approvals, reconciliation. That validates the market. But autonomous agent spend has to be governed before the agent acts, not only before the bill hits. Different primitive, different product.
Employees, vendors, cards, invoices, finance workflows. The decision happens after the agent has acted — on the statement, in the approval queue, at month-end close.
Agents, tasks, tool calls, policy checks, budget reservations, protocol payments, audit receipts. The decision happens when an agent is about to call a paid API, invoke a paid MCP tool, start an MPP session, or hit an x402 endpoint.
Drop the Agentagon client into your agent and replace your provider calls with client.call(). Every invocation is checked against your team budgets, agent caps, and provider allowlists before a dollar moves.
import agentagon client = agentagon.Client(pat="ag_pat_...") # Every call is policy-checked before it spends a dollar. result = client.call( provider="openai", payload={"model": "gpt-5", "input": "..."}, ) # Spend logged. Budget tracked. Audit ready. # Over a cap? Routed to your approval queue automatically.
Six surfaces, one workspace. Built for the people accountable for AI agent spend, security, and audit.
Real-time spend, policy adherence, active agents, approvals pending, blocked events. Spend trend by day. Breakdowns by team, employee, agent, and provider. One source of truth for the whole org.
Team budgets, employee delegation, agent caps, provider allowlists, approval thresholds. Escalation chain runs Agent to Employee owner to Team budget owner to CFO/CTO. Everything is enforced before a dollar moves.
OpenAI, Anthropic, Stripe, Plaid, Pinecone, Serper, Context7, and more. Each one shows protocols supported, pricing model, p50 latency, approval status, and ROI score. Route spend over the right rail without hand-rolling integrations.
x402 and MPP for HTTP-native and machine payments. Stripe and Plaid for cards, settlements, and bank context. Live USDC balances on Base and Solana. Top up, pay, and reconcile in one place.
High, Medium, Low queues by spend impact. Approve in dashboard, Slack, or via API. Agents pause on approval, resume on approve, deny on deny, all with full audit trail. No more screenshots in a Slack thread.
Audited event ledger with CSV export. ERP export to NetSuite. SSO and SCIM via WorkOS. Slack notifications. MCP for tool interop. Compliance-ready reports for finance and security.
Once your data is flowing, Agentagon surfaces lower-cost provider and model alternatives at par quality, and learns from spend patterns to optimize ROI. Same prompt routed to a model that costs 60% less — with your policy still in front.
Stripe, Plaid, x402, MPP. Live in minutes via SDK or proxy. No infra to host, no chains to learn. Your existing providers and accounts plug straight in.
Create teams. Delegate to employees. Mint agents. Cap spend, allowlist providers, set approval thresholds. The 5-layer policy stack runs on every call.
Use agentagon.Client(pat=...).call(...) in your code. Every call is policy-checked, budget-enforced, and audit-logged before it lands at the provider.
Real-time spend, blocked events, approvals queue, and audit log. Alerts in Slack. Exports to ERP. Finance, IT, and engineering see the same data, scoped to what they own.
Every other spend tool assumes a human at a checkout. Agentagon assumes an agent at an API.
Finance gets a single ledger. Every rail, every provider, every agent, in one audited stream. Reconcile in minutes, not weeks. Export to NetSuite or your warehouse.
Security gets a real control plane. RBAC, SSO, SCIM, allowlists, kill switches. Block a runaway agent in one click. Produce an audit trail on demand.
Engineering gets to ship. One SDK call replaces a checkout flow per provider, a wallet per chain, and a billing integration per rail. The infrastructure is already there.
40% of enterprise apps will run task-specific AI agents by end of 2026, up from less than 5% in 2025. Each agent generates thousands of paid calls per minute.
$15T of B2B spend will be intermediated by AI agents by 2028. Today, almost none of it is governed, audited, or capped at the agent layer.
25% of developers now design APIs for AI agents. Payment rails are maturing fast (x402, MPP, ACP). The control plane is the missing layer.
We're onboarding design partners deploying AI agents in production, including teams already spending $2M+/year on AI. If finance, security, or engineering owns this surface at your company, we want to talk.