import Link from 'next/link';
import RoiCta from '../../components/RoiCta';
import { ArrowLeft, Calendar, Clock } from 'lucide-react';

export const metadata = {
  title: 'AI Agent API Cost Control: Stop Runaway Spend Before API Calls Execute',
  description: 'Control AI agent API costs with request-path budget checks, tool pricing, delegated spend limits, revocation, and economic firewalls before calls execute.',
  alternates: { canonical: 'https://satgate.io/blog/ai-agent-api-cost-control' },
  keywords: [
    'AI agent API cost control',
    'AI agent budget enforcement',
    'API cost management',
    'rate limiting vs budget control',
    'AI agent spending caps',
  ],
  openGraph: {
    title: 'AI Agent API Cost Control: Stop Runaway Spend Before Calls',
    description: 'Control AI agent API costs with request-path budget checks, tool pricing, spend limits, revocation, and economic firewalls.',
    url: 'https://satgate.io/blog/ai-agent-api-cost-control',
    type: 'article',
    publishedTime: '2026-03-05T00:00:00Z',
  },
  twitter: {
    card: 'summary_large_image',
    title: 'AI Agent API Cost Control: Stop Runaway Spend Before Calls',
    description: 'Stop runaway AI agent API spend with budget checks, tool pricing, spend caps, revocation, and economic firewalls.',
  },
};

export default function AiAgentApiCostControlPage() {
  const jsonLd = {
    '@context': 'https://schema.org',
    '@type': 'TechArticle',
    headline: 'AI Agent API Cost Control: Stop Runaway Spend Before API Calls Execute',
    description: metadata.description,
    author: { '@type': 'Organization', name: 'SatGate' },
    publisher: { '@type': 'Organization', name: 'SatGate', url: 'https://satgate.io' },
    datePublished: '2026-03-05',
    dateModified: '2026-05-04',
    mainEntityOfPage: 'https://satgate.io/blog/ai-agent-api-cost-control',
    about: [
      { '@type': 'Thing', name: 'AI agent API cost control' },
      { '@type': 'Thing', name: 'request-path budget checks' },
      { '@type': 'Thing', name: 'rate limiting versus budget enforcement' },
      { '@type': 'Thing', name: 'delegated agent spend limits' },
      { '@type': 'Thing', name: 'economic firewalls for API calls' },
    ],
  };

  const faqSchema = {
    '@context': 'https://schema.org',
    '@type': 'FAQPage',
    mainEntity: [
      {
        '@type': 'Question',
        name: 'How do you control AI agent API costs?',
        acceptedAnswer: {
          '@type': 'Answer',
          text: 'Control AI agent API costs by putting an enforcement layer in the request path. The layer should identify the agent, price the API or tool call, check remaining budget, apply route/model policy, and block or downgrade requests before upstream spend occurs.',
        },
      },
      {
        '@type': 'Question',
        name: 'Why are API rate limits not enough for AI agent cost control?',
        acceptedAnswer: {
          '@type': 'Answer',
          text: 'Rate limits count requests, but AI agent costs depend on models, tools, routes, retries, delegation, and token volume. Ten cheap requests may cost less than one premium model call, so spend needs budget enforcement instead of request-count throttling alone.',
        },
      },
      {
        '@type': 'Question',
        name: 'What is the difference between an economic firewall and a rate limit?',
        acceptedAnswer: {
          '@type': 'Answer',
          text: 'A rate limit controls traffic volume. An economic firewall controls spend and authority. It checks budgets, prices, capability scopes, revocation, and audit requirements before an autonomous agent reaches an API, model, or MCP tool.',
        },
      },
      {
        '@type': 'Question',
        name: 'When should AI agent API cost controls block a request?',
        acceptedAnswer: {
          '@type': 'Answer',
          text: 'AI agent API cost controls should block a request before execution when the estimated cost exceeds the remaining budget, the tool or route is outside scope, the credential is revoked or expired, or a delegated sub-agent would exceed its parent allowance.',
        },
      },
    ],
  };

  return (
    <div className="min-h-screen bg-black text-gray-100 font-sans">
      <script type="application/ld+json" dangerouslySetInnerHTML={{ __html: JSON.stringify(jsonLd) }} />
      <script type="application/ld+json" dangerouslySetInnerHTML={{ __html: JSON.stringify(faqSchema) }} />

      <div className="max-w-3xl mx-auto px-6 py-16">
        <Link href="/blog" className="text-gray-500 hover:text-white flex items-center gap-2 transition mb-8">
          <ArrowLeft size={18} /> Back to Blog
        </Link>

        <header className="mb-12">
          <div className="flex flex-wrap gap-2 mb-4">
            <span className="px-2 py-1 rounded-full bg-purple-900/30 border border-purple-500/30 text-purple-300 text-xs font-mono">Cost Control</span>
            <span className="px-2 py-1 rounded-full bg-cyan-900/30 border border-cyan-500/30 text-cyan-300 text-xs font-mono">AI Agents</span>
            <span className="px-2 py-1 rounded-full bg-yellow-900/30 border border-yellow-500/30 text-yellow-300 text-xs font-mono">Economic Firewall</span>
          </div>

          <h1 className="text-4xl font-bold mb-4">How to Control AI Agent API Costs: Rate Limiting vs Economic Firewalls</h1>

          <p className="text-xl text-gray-400 mb-6 italic">
            Rate limits count requests. Economic firewalls control money. Autonomous agents need the second one.
          </p>

          <div className="flex items-center gap-4 text-sm text-gray-500">
            <span className="flex items-center gap-1"><Calendar size={14} /> March 5, 2026</span>
            <span className="flex items-center gap-1"><Clock size={14} /> 8 min read</span>
          </div>
        </header>

        <article className="prose prose-invert prose-lg max-w-none">
          <p className="text-gray-300 text-lg leading-relaxed">
            The old API cost-control playbook was built for humans and predictable applications. Put a monthly provider budget on the account. Add a rate limit to stop abuse. Watch a dashboard. Send an alert when usage spikes.
          </p>

          <p className="text-gray-300 leading-relaxed">
            That playbook breaks when the caller is an autonomous agent. Agents do not just make one request. They plan, retry, delegate, call tools, summarize outputs, and loop. A single task can fan out into hundreds or thousands of billable API calls before anyone sees the dashboard.
          </p>

          <p className="text-gray-300 leading-relaxed">
            The real question is no longer <strong className="text-white">how many requests are allowed?</strong> It is <strong className="text-white">how much is this agent allowed to spend?</strong>
          </p>

          <h2 className="text-2xl font-bold text-white mt-12 mb-4">Why rate limits are the wrong primitive</h2>

          <p className="text-gray-300 leading-relaxed">
            Rate limits are useful, but they are not economic controls. They usually answer questions like:
          </p>

          <ul className="space-y-2 text-gray-300">
            <li>How many requests per minute can this API key make?</li>
            <li>How much traffic can this IP send?</li>
            <li>Should this user be throttled?</li>
          </ul>

          <p className="text-gray-300 leading-relaxed">
            None of those questions map cleanly to AI agent cost. Ten cheap requests might cost less than one expensive model call. One retrieval tool call might be free, while one code-generation call might trigger a long GPT response, a search operation, a database query, and a paid API call. Counting requests misses the economic shape of the workload.
          </p>

          <div className="my-8 rounded-xl border border-red-900/50 bg-red-950/20 p-6">
            <p className="text-red-200 font-semibold mb-2">The failure mode:</p>
            <p className="text-gray-300 mb-0">
              A rate limit can say an agent is allowed to make 1,000 requests. It cannot say those 1,000 requests may only spend $25, may only call the premium tool 5 times, or must stop immediately when a delegated sub-agent exhausts its budget.
            </p>
          </div>

          <h2 className="text-2xl font-bold text-white mt-12 mb-4">What AI agent API cost control requires</h2>

          <p className="text-gray-300 leading-relaxed">
            AI agent cost control has to happen in the request path, before the upstream API is called. That enforcement layer needs to understand agent identity, policy, budget, tool cost, provider route, and delegated authority.
          </p>

          <p className="text-gray-300 leading-relaxed">
            In practice, that means every request should answer six questions before it moves forward:
          </p>

          <ol className="space-y-3 text-gray-300">
            <li><strong className="text-white">Who is calling?</strong> Identify the agent, tenant, team, task, or delegated sub-agent.</li>
            <li><strong className="text-white">What can it access?</strong> Enforce allow, deny, revoke, and expiry policy.</li>
            <li><strong className="text-white">What will this cost?</strong> Estimate or assign request/tool/provider cost before forwarding.</li>
            <li><strong className="text-white">What budget remains?</strong> Check per-agent, per-tool, per-session, or per-day limits.</li>
            <li><strong className="text-white">Should this route change?</strong> Route cheap tasks to lower-cost providers and reserve premium models for high-value work.</li>
            <li><strong className="text-white">What should be recorded?</strong> Produce an Evidence Pack with identity, spend, policy decision, and outcome.</li>
          </ol>

          <h2 className="text-2xl font-bold text-white mt-12 mb-4">Economic firewalls: budget enforcement at the gateway layer</h2>

          <p className="text-gray-300 leading-relaxed">
            An <Link href="/economic-firewall" className="text-cyan-400 hover:text-cyan-300">economic firewall</Link> is the missing layer between autonomous agents and billable APIs. It sits inline, checks the policy attached to the agent capability, and decides whether the request should be observed, controlled, charged, routed, or blocked.
          </p>

          <p className="text-gray-300 leading-relaxed">
            Unlike a dashboard, it acts before the bill arrives. Unlike a rate limiter, it understands money. Unlike a static API key, it can carry caveats: this agent may spend up to 500 credits, only on this route, before this expiry, and only while delegated by this parent workflow.
          </p>

          <div className="my-8 rounded-xl border border-cyan-700/50 bg-cyan-950/20 p-6">
            <p className="text-cyan-200 font-semibold mb-2">SatGate pattern:</p>
            <p className="text-gray-300 mb-0">
              Observe first to learn real cost. Control next with hard caps and revocation. Charge when the API itself becomes a product for external agents or paid agents.
            </p>
          </div>

          <h2 className="text-2xl font-bold text-white mt-12 mb-4">A simple policy model</h2>

          <p className="text-gray-300 leading-relaxed">
            A practical agent cost-control policy should be readable by engineers, finance, and security. It might look like this conceptually:
          </p>

          <pre className="bg-gray-950 border border-gray-800 rounded-lg p-4 overflow-x-auto text-sm text-gray-300"><code>{`agent: research-bot
route: /v1/responses
provider: openai
mode: control
budget:
  daily: 25.00 USD
  per_request: 0.50 USD
  premium_tool_calls: 10
on_exhausted: block
audit:
  include: [agent, task, model, route, estimated_cost, decision]`}</code></pre>

          <p className="text-gray-300 leading-relaxed">
            The exact syntax can vary. The important part is the enforcement point. If the policy is only in a spreadsheet, dashboard, or Slack alert, it is advice. If it is checked before the API call, it is control.
          </p>

          <h2 className="text-2xl font-bold text-white mt-12 mb-4">Where teams should start</h2>

          <p className="text-gray-300 leading-relaxed">
            Do not begin by guessing perfect prices. Start with visibility. Put agent API traffic through an economic gateway in Observe mode. Attribute spend by agent, model, route, and tool. Find the workflows with the worst cost-to-value ratio.
          </p>

          <p className="text-gray-300 leading-relaxed">
            Then move the riskiest paths into Control mode. Add per-agent budgets, per-request ceilings, and revocation. Finally, when you expose APIs to external autonomous agents, add Charge so the same request path can collect payment before access.
          </p>

          <p className="text-gray-300 leading-relaxed">
            That is the difference between watching AI agent costs and governing them.
          </p>

          <div className="my-8 rounded-2xl border border-purple-900/60 bg-purple-950/20 p-6">
            <h2 className="text-2xl font-bold text-white mb-4">AI Agent API Cost Control FAQ</h2>
            <div className="space-y-5">
              <div>
                <h3 className="text-xl font-bold text-white mb-2">How do you control AI agent API costs?</h3>
                <p className="text-gray-300 leading-relaxed mb-0">
                  Put an enforcement layer in the request path. It should identify the agent, price the API or tool call, check remaining budget, apply route/model policy, and block or downgrade requests before upstream spend occurs.
                </p>
              </div>
              <div>
                <h3 className="text-xl font-bold text-white mb-2">Why are API rate limits not enough for AI agent cost control?</h3>
                <p className="text-gray-300 leading-relaxed mb-0">
                  Rate limits count requests, but agent costs depend on models, tools, routes, retries, delegation, and token volume. Ten cheap requests may cost less than one premium model call, so spend needs budget enforcement rather than request-count throttling alone.
                </p>
              </div>
              <div>
                <h3 className="text-xl font-bold text-white mb-2">What is the difference between an economic firewall and a rate limit?</h3>
                <p className="text-gray-300 leading-relaxed mb-0">
                  A rate limit controls traffic volume. An economic firewall controls spend and authority by checking budgets, prices, capability scopes, revocation, and audit requirements before an agent reaches an API, model, or MCP tool.
                </p>
              </div>
              <div>
                <h3 className="text-xl font-bold text-white mb-2">When should AI agent API cost controls block a request?</h3>
                <p className="text-gray-300 leading-relaxed mb-0">
                  They should block before execution when the estimated cost exceeds remaining budget, the tool or route is outside scope, the credential is revoked or expired, or a delegated sub-agent would exceed its parent allowance.
                </p>
              </div>
            </div>
          </div>

          <RoiCta />

          <div className="mt-12 rounded-xl border border-gray-800 bg-gray-950/70 p-6">
            <h2 className="text-xl font-bold text-white mt-0">Related guides</h2>
            <ul className="space-y-2 mb-0">
              <li><Link href="/blog/llm-cost-management" className="text-cyan-400 hover:text-cyan-300">LLM Cost Management: Dashboards vs Real-Time Budget Enforcement</Link></li>
              <li><Link href="/blog/mcp-budget-enforcement-guide" className="text-cyan-400 hover:text-cyan-300">MCP Budget Enforcement: A Practical Guide</Link></li>
              <li><Link href="/blog/how-to-add-budget-limits-to-openai-api-calls" className="text-cyan-400 hover:text-cyan-300">OpenAI API Budget Limits</Link></li>
              <li><Link href="/blog/what-is-an-economic-firewall" className="text-cyan-400 hover:text-cyan-300">What Is an Economic Firewall?</Link></li>
            </ul>
          </div>
        </article>
      </div>
    </div>
  );
}
