Agent spending limits should stop the next request, not explain the last bill
Autonomous agents need hard spending limits that apply per task, workflow, delegated sub-agent, model, tool, API route, and time window. SatGate enforces authority before execution and records a receipt for every budget decision.
The control point is before the call
Autonomous agents can generate real costs through model calls, API requests, MCP tools, delegated sub-agents, retries, and background workflows. If the policy check happens after the request, the money is already spent.
SatGate enforces economic policy at the gateway boundary. Every important request can be evaluated against budget, scope, identity, revocation, route, tool, receipt, and audit rules before upstream access.
That is the difference between cost reporting and economic control.
What good policy includes
Per-agent budgets
Track and limit spend by agent identity, tenant, task, workflow, route, model, and MCP tool.
Delegated sub-agent limits
Give sub-agents smaller budgets, narrower tools, shorter expiry, and separate Evidence Packs than their parent.
Kill switches and revocation
Stop future spend by revoking or narrowing credentials before the next API or MCP request.
From Observe to Control
Start by measuring real agent spend, then enforce hard caps where risk, cost, or autonomy justifies it.
SatGate controls
Use SatGate to move from Observe to Control: measure real agent economics first, then enforce the limits that matter.
Agent identity
Attribute requests to the tenant, agent, task, workflow, route, model, MCP tool, and delegated sub-agent.
Budget checks
Evaluate remaining spend, per-request ceilings, daily caps, tool caps, and route budgets before forwarding.
Scoped credentials
Use expiring capabilities instead of broad static keys so authority matches the job.
Revocation
Block the next request when a credential, workflow, route, budget, or agent should stop.
Audit trails
Record allow/deny decisions with policy, budget remaining, route, tool, estimated cost, and outcome.
Benchmark risk
Model loops, retry storms, fanout, detection delay, and avoided spend with benchmark-backed scenarios.
FAQ
Agent spending limit questions
What are agent spending limits?
Agent spending limits are request-path budgets and caps that constrain what autonomous AI agents can spend by task, route, tool, model, workflow, tenant, session, or day before requests execute.
Why are dashboards not enough?
Dashboards and billing alerts report spend after requests complete. Autonomous agents can loop, retry, and delegate fast enough that budget policy must be enforced before upstream access.
How does SatGate help?
SatGate sits in the request path and checks identity, budget, route, tool scope, credential caveats, expiry, revocation, and audit policy before forwarding the request.
What spending limits should AI agents have?
AI agents should have spending limits by tenant, agent, task, workflow, session, model, tool, route, delegated sub-agent, and time window, with per-request ceilings and emergency revocation.
Are spending limits better than rate limits for AI agents?
They solve different problems. Rate limits control frequency, while spending limits control economic exposure by checking request price, remaining budget, scope, and policy before cost is created.
Make agent economics enforceable.
SatGate is the economic firewall for AI agents: observe every request, enforce spending limits before execution, and preserve receipts for budget, revocation, and access decisions.