Topic

Rate Limiting

All digests tagged Rate Limiting

Give the Agent a Budget, Not a Token — Sachin Malhotra, Anthropic thumbnail

· 19:53

Give the Agent a Budget, Not a Token — Sachin Malhotra, Anthropic

The presentation argues that granting AI agents 'unbounded power' via simple tokens is dangerous. Instead of narrowing the token scope (a boolean fix), engineers must implement a comprehensive 'budget' system with four dimensions: how much, how fast, what can be undone, and who notices. Key solutions include using asymmetric verbs (prioritizing loud failures), enforcing rate limits on writes, implementing trip wires over static allow lists, and utilizing an 'undo test' to size the overall safety policy.

Key takeaways

  1. Budget vs. Token 7:03

    A token is a boolean (yes/no) scope; a budget is multi-dimensional, considering volume, velocity, reversibility, and observability. The failure was giving the agent unbounded power, not the model itself.

  2. Asymmetric Verbs 10:05

    Prioritize granting agents access to operations that fail loudly (e.g., unskipping a test, which causes CI to go red) and keep critical failure verbs (like skipping a test) reserved for human intervention with an audit trail.

  3. Rate Limits & Trip Wires 13:54

    Implement rate limits on every write operation, ensuring the ceiling refills automatically. Use trip wires (monitoring aggregate behavior) instead of static allow lists, as trip wires adapt to real-world data.

  4. The Undo Test

    This test asks if the agent can autonomously roll back its own changes and what the blast radius would be if it failed. If not, a second key (human involvement) and an audit record are required.

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Designing REST APIs for the age of AI agents - Boyan Mihaylov - NDC Copenhagen 2026 thumbnail

· 43:22

Designing REST APIs for the age of AI agents - Boyan Mihaylov - NDC Copenhagen 2026

The talk argues that REST APIs, originally designed for human developers, must fundamentally adapt to serve AI agents and LLMs as primary consumers. To ensure reliability and discoverability in an AI-driven world, API designers must focus on structured documentation (OpenAPI), robust error handling, maintaining consistency, implementing adaptive rate limiting, and considering new standards like the Model Context Protocol (MCP) for web integration.

Key takeaways

  1. AI Agents are a New Consumer 21:45

    The rise of AI tools means that API consumers are shifting from human developers to autonomous agents. These agents will interact with APIs by generating requests and chaining calls, requiring the API to be machine-readable and reliable.

  2. Documentation is Critical for AI 26:45

    The OpenAPI standard (JSON or YAML specification) is crucial. Beyond simply documenting endpoints, developers must add rich metadata about the API's purpose, constraints, and potential errors to minimize agent hallucination.

  3. Prioritize Error Handling 30:30

    Instead of basic validation messages, provide detailed error information (e.g., specifying the problematic field and supported options) to allow AI agents to self-correct and retry requests effectively.

  4. Adopt Adaptive Rate Limiting 35:05

    Traditional static rate limiting (e.g., fixed quotas per minute) is insufficient for unpredictable AI agent traffic. Implement adaptive strategies that analyze traffic patterns and adjust limits dynamically to maintain service availability.

  5. Consider Web MCP 40:05

    For web-based services, the Model Context Protocol (MCP) is an emerging standard allowing a webpage itself to expose tools and workflows directly to AI agents, making the entire page functional rather than just relying on backend APIs.

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