Agent-First API Design: How to Build APIs That AI Agents Actually Understand

Most APIs are designed for human developers who read documentation, browse examples, and debug with stack traces. But in 2026, the fastest-growing API consumers aren't humans — they're AI agents. And they interact with APIs very differently.

This is the story of how we redesigned LemonData's unified AI API around a simple principle: don't be smart, be informative. The result is what we call agent-first API design — and it cut our users' wasted tokens by over 60%.

What Is Agent-First API Design?

Agent-first API design means structuring your API responses — especially error responses — so that an AI agent can understand what went wrong and fix it without external help.

Traditional API error:

{"error": {"message": "Model not found"}}

Agent-first API error:

{
  "error": {
    "code": "model_not_found",
    "message": "Model 'gpt5' not found",
    "did_you_mean": "gpt-4o",
    "suggestions": [{"id": "gpt-4o"}, {"id": "gpt-4o-mini"}],
    "hint": "Use GET /v1/models to list all available models."
  }
}

The difference? With a traditional API, the agent needs to search the web, find documentation, parse HTML, and guess. With an agent-first API, it self-corrects in one step.

Why Traditional APIs Fail AI Agents

Watch what happens when an AI agent hits a typical API aggregator for the first time:

Agent: POST /v1/chat/completions {"model": "gpt5"}
API:   400 {"error": {"message": "Model not found"}}
Agent: (searches the web for "lemondata models list")
Agent: (fetches a docs page, maybe the wrong one)
Agent: (parses HTML, finds a model name)
Agent: POST /v1/chat/completions {"model": "gpt-4o"}
API:   200 ✓

Six steps. Multiple network requests. Hundreds of wasted tokens. And this is the happy path — the agent guessed the right docs URL.

With agent-first design:

Agent: POST /v1/chat/completions {"model": "gpt5"}
API:   400 {"did_you_mean": "gpt-4o", "hint": "Use GET /v1/models..."}
Agent: POST /v1/chat/completions {"model": "gpt-4o"}
API:   200 ✓

Two steps. Zero web searches. The agent self-corrected from the error response alone.

The Core Principle: Intelligence Stays on the Model Side

The temptation is to build "smart" APIs — auto-correct the model name, silently route to a similar model, add a recommendation engine. We rejected all of that.

When an agent sends model: "gpt5", you don't know its intent. Maybe it's testing if GPT-5 exists. Maybe it has a budget constraint. Maybe it needs a specific capability. Auto-routing to gpt-4o would silently change the cost, the output quality, and the capabilities — without the agent knowing.

The right move is to fail fast and fail informatively. Give the agent all the data. Let it decide.

Four Agent-First API Design Patterns

Pattern 1: Model Not Found → Fuzzy Suggestions

{
  "error": {
    "code": "model_not_found",
    "did_you_mean": "gpt-4-turbo",
    "suggestions": [
      {"id": "gpt-4o"},
      {"id": "gpt-4o-mini"},
      {"id": "claude-sonnet-4-5"}
    ],
    "hint": "Did you mean 'gpt-4-turbo'? Use GET /v1/models to list all available models."
  }
}

The did_you_mean field uses a three-layer resolution: static alias mapping (from production data, not guesswork), normalized string matching, and bounded edit distance. All candidates are validated against the live model list — we never suggest a model that's currently offline.

Pattern 2: Insufficient Balance → Budget-Aware Alternatives

{
  "error": {
    "code": "insufficient_balance",
    "balance_usd": 0.12,
    "estimated_cost_usd": 0.35,
    "suggestions": [
      {"id": "gpt-4o-mini", "estimated_cost_usd": 0.02},
      {"id": "deepseek-chat", "estimated_cost_usd": 0.01}
    ],
    "hint": "Insufficient balance. Try a cheaper model or top up."
  }
}

Instead of just saying "not enough money," we tell the agent exactly how much it has, how much it needs, and which models it can afford. The agent can autonomously downgrade to a cheaper AI model — no human intervention needed.

Pattern 3: All Channels Failed → Live Alternatives

{
  "error": {
    "code": "all_channels_failed",
    "retryable": true,
    "retry_after": 30,
    "alternatives": [
      {"id": "claude-sonnet-4-5", "status": "available"},
      {"id": "gpt-4o", "status": "available"}
    ],
    "hint": "All channels for 'claude-opus-4-6' temporarily unavailable. Retry in 30s or try an alternative."
  }
}

The alternatives list isn't static — it's a live query against our channel health data. The agent gets real-time information about what's actually working right now.

Pattern 4: Rate Limited → Exact Retry Timing

{
  "error": {
    "code": "rate_limit_exceeded",
    "retryable": true,
    "retry_after": 8,
    "limit": "1000/min",
    "remaining": 0,
    "hint": "Rate limited. Retry after 8s."
  }
}

No guessing. No exponential backoff starting from arbitrary values. The agent knows the exact wait time. For more on handling rate limits effectively, see our AI API rate limiting guide.

Success Responses Carry Hints Too

When an agent calls /v1/chat/completions with a Claude model, the response includes:

X-LemonData-Hint: This model supports native Anthropic format. Use POST /v1/messages for better performance.
X-LemonData-Native-Endpoint: /v1/messages

We're telling the agent: "this worked, but there's a better way." The agent can switch to the native endpoint on the next call — getting access to features like extended thinking and prompt caching that aren't available through the OpenAI-compatible format.

We put this in headers, not the response body, because the body follows the OpenAI/Anthropic spec. Headers are the safe extension point.

The /v1/models Response as Agent Cheat Sheet

We added three fields to every model in the /v1/models response:

• category — chat model, image generator, video model, or audio? No more guessing from the name.
• pricing_unit — per token, per image, per second, per request. Essential for cost estimation.
• cache_pricing — upstream prompt cache prices plus platform semantic cache discount.

Combined with existing fields (pricing, capabilities, aliases, max tokens), an agent can make fully informed model selection decisions from a single API call.

llms.txt: The Agent's First Read

We serve a dynamic llms.txt at api.lemondata.cc/llms.txt — a machine-readable overview of the entire API. It includes:

• A first-call template with working code
• Common model names (auto-generated from usage data, not hardcoded)
• All 12 endpoints with parameters
• Filter parameters for model discovery

An agent that reads this file before its first API call will likely get it right on the first try.

Data-Driven, Not Knowledge-Driven

Every suggestion in our system comes from production data. The did_you_mean alias map was seeded from 30 days of actual model_not_found errors in our request logs. Model suggestions are sorted by real usage patterns. The "common model names" in llms.txt are generated from our database.

We track every model miss in a Redis sorted set. When a misspelling accumulates enough hits, it gets promoted to the alias map. When a model goes offline, it automatically disappears from all suggestions. The system improves itself.

The Design Constraint That Made It Work

We set one rule: no new endpoints, no new SDKs, no breaking changes. Everything had to work within the existing OpenAI-compatible error format. New fields are optional — any client that ignores them gets the same experience as before.

This constraint forced us to be precise about what information actually helps an agent self-correct, rather than building elaborate new APIs that nobody would adopt.

How to Apply Agent-First Design to Your Own API

If you're building APIs that AI agents will consume:

1. Every error should be actionable — include what went wrong, why, and what to do next
2. Suggest alternatives, don't auto-correct — let the agent make informed decisions
3. Use structured fields, not prose — did_you_mean is parseable, "Did you mean..." in a string is not
4. Ground suggestions in real data — production usage patterns beat hardcoded lists
5. Serve machine-readable discovery — llms.txt, OpenAPI specs, or structured model lists
6. Maintain backward compatibility — new hint fields should be additive, never breaking

FAQ

What is agent-first API design?

Agent-first API design is an approach where error responses include structured, machine-readable hints that allow AI agents to self-correct without human intervention or external documentation lookups.

How is agent-first different from developer-first API design?

Developer-first APIs optimize for human readability: clear error messages, good documentation, helpful examples. Agent-first APIs add structured fields (did_you_mean, suggestions, hint) that machines can parse and act on programmatically.

Does agent-first design break existing clients?

No. Agent-first fields are additive — extra fields in the JSON response. Clients that don't know about them simply ignore them. Existing integrations continue to work unchanged.

How does LemonData implement agent-first design?

LemonData's unified AI API gateway adds structured error hints to all 300+ models. Every error response includes actionable suggestions, and the llms.txt endpoint provides machine-readable API discovery.

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LemonData provides unified access to 300+ AI models through a single API. Try the agent-first API at lemondata.cc.