I've been watching developers struggle with the same question lately: "How do I let AI agents call my APIs without accidentally nuking my production database?"
It's a valid concern. AI agents are powerful, unpredictable, and often operate with the subtlety of a bull in a china shop. Give them unrestricted access to your backend, and you're basically handing a toddler the keys to your car.
But here's the thing: AI agents aren't going away. They're becoming the primary interface for how software gets built, deployed, and operated. So the question isn't whether to integrate them, it's how to do it successfully.
In this article, we discuss a few commonly asked questions and the best practices we recommend.
How do I let an AI call my APIs safely?
You need authentication, authorization, and input validation—but designed specifically for AI behavior patterns, not human ones.
Traditional API security assumes humans are making requests. Humans make mistakes, but they're generally predictable. They don't suddenly decide to call your delete endpoint 10,000 times in a row because they misunderstood the instructions. An AI agent might, though.
So for AI to call your APIs safely, your backend needs a few critical guardrails:
- Request signing: Every AI request should be cryptographically signed with a unique key per agent. This isn't just authentication—it's attribution. When something goes wrong, you need to know which agent did what.
- Capability-based permissions: Instead of role-based access, think in terms of capabilities. An AI agent handling customer support might have "read customer data" and "create support ticket" capabilities, but never "delete account."
- Context-aware rate limiting: Traditional rate limiting counts requests per IP or user. AI rate limiting needs to understand intent. An agent making 100 legitimate data queries is different from one making 100 deletion attempts.
How do I prevent an AI from taking destructive actions?
I recently covered agentic security in the below Rule of Two video covering secure architecture to prevent an agent from making “bad decisions.” Similarly, the concept of separation can be applied in this context, too.
Implement a three-tier safety system: prevention, confirmation, and rollback:
- Prevention: Certain actions should never be directly accessible to AI agents. Deletion, data exports, permission changes. These should always require human confirmation or be completely off-limits.
- Confirmation: For potentially destructive actions, implement a confirmation loop. The AI can request the action, but a human has to approve it. This works especially well for batch operations or anything involving customer data.
- Rollback: Every AI action should be reversible. This means comprehensive audit logs, soft deletes, and versioned data. If an agent screws up, you need to be able to undo it quickly.
The key insight here is that AI safety isn't about perfect prevention, it's about fast recovery. (Check out some more tips about safely integrating AI into your SDLC!)
How do I validate AI-generated input?
Treat AI input like user input, but with steroids. It needs the same validation, plus additional checks for AI-specific failure modes.
AI agents are creative in ways humans aren't. They'll send you JSON that's technically valid but semantically nonsense. They'll generate IDs that look real but reference non-existent records. They'll create circular references that would make your database cry.
Your validation layer needs 5 types of validation:
- Schema validation: Types, required fields, format checking.
- Semantic validation: Does this data make sense in context? If the AI is updating a user's email, is it actually an email format? If it's setting a date, is it in the future when it should be?
- Relationship validation: AI agents love to reference things that don't exist. Every foreign key needs to be checked. Every relationship needs to be verified.
- Business rule validation: This is where AI gets tricky. An AI might try to create a subscription for a user who's already been banned, or schedule an event in the past. Your backend needs to understand your business rules and enforce them consistently.
In traditional stacks, this usually turns into a sprawl of middleware, handwritten guardrails, and hard-to-trace preconditions buried in code. This isn’t good enough.
When agents are generating requests at machine speed, you can’t rely on black-box functions and logs after the fact. You need to see what the backend is about to do—how the data flows, which rules fire, what records are touched, and where execution stops if something fails. This is where visual validation becomes critical. Instead of hoping your custom security functions caught everything, a visual logic layer lets you inspect the full execution path before it reaches production: inputs, transformations, permission checks, relationship lookups, and business rules—step by step. You can reason about what the AI attempted, spot edge cases humans didn’t anticipate, and tighten constraints without spelunking through opaque code paths.
If you can’t trace and validate the behavior end-to-end, you don’t really control what the agent is doing. And for safe agentic engineering, control is the difference between automation and liability.
How do I control cost when AI hits my backend?
Cost control for agentically engineered backends is different from traditional methods because AI usage patterns are fundamentally unpredictable. Here are some things to keep in mind:
- Predictive budgeting: Instead of traditional usage-based pricing, implement token budget caps per agent or per operation type. An AI agent should never be able to accidentally spend your entire monthly budget in an afternoon, and limiting token spend can help.
- Operation weighting: Not all API calls are created equal. A simple read might cost 100 tokens, while a complex report generation costs 10000 tokens. As the operator, you need to understand these costs and factor them into agentic decision-making.
- Circuit breakers: When agents are stuck in a loop, it’s important to stop their operations so they don’t continually burn tokens and get caught in a destructive, non-forward moving loop.
How do I store AI state and memory?
AI agents need persistent memory, but traditional session storage isn't sufficient. They need context that persists across conversations, tasks, and even different agent instances.
The trick is designing memory storage that's both accessible to AI and auditable by humans.
This looks like persistent storage using a database! It’s got:
- Structured context: Store AI memory in a structured format—not just free text. Include metadata about when the memory was created, what triggered it, and how confident the AI was about the information.
- Scoped memory: Different types of AI operations need different memory scopes. A customer service agent needs access to conversation history but not to internal system diagnostics.
- Memory expiration: AI memory should have TTL (time to live) policies. Old context can become misleading or irrelevant. Build in automatic cleanup.
How do I orchestrate multiple AI agents?
Multi-agent systems are where things get really interesting… and really complicated. You're essentially building a distributed system where the nodes are unpredictable. Here are some things to keep in mind:
- Event-driven architecture: Agents should communicate through events, not direct calls. When Agent A completes a task, it publishes an event. Agent B subscribes to that event type and reacts accordingly.
- State machines: Define clear states for multi-agent workflows. "Data Collection," "Processing," "Review," "Complete." Each agent knows what state the workflow is in and what their role should be.
- Coordination primitives: Sometimes agents need to coordinate directly. Implement things like distributed locks, queues, and semaphores that AI agents can use to avoid stepping on each other.
If we provide the infrastructure for safe coordination, then we reduce our exposure to risk and things going awry.
How do I build tool-calling APIs?
Tool-calling APIs are the interface between AI reasoning and real-world actions. They need to be both powerful and safe. This means:
- Declarative design: Instead of exposing low-level database operations, expose high-level business operations. Don't give the
AI UPDATE users SET...Give itchange_user_email(user_id, new_email). - Idempotent operations: AI agents might retry operations or call them multiple times. Your tool APIs should handle this gracefully. The same operation called twice should have the same effect as calling it once. This can be enforced with prompts, or logic mechanisms that track state.
- Rich error responses: When something goes wrong, the AI needs enough context to understand why and potentially fix it. Generic error messages don't help. Specific, actionable error responses do. Simplified, this looks like concise
How do I rate-limit AI usage?
AI rate limiting is more complex than traditional rate limiting because AI behavior is less predictable and often bursty. In practice, AI limits usually fall into two categories: total executions and executions within a time window.
Prompt instructions can help guide behavior, but production systems need enforcement at the infrastructure level.
Limiting total executions
Here are some limits to enforce:
- Step Limits (Loop Protection): Set a maximum number of tool calls or actions an agent can perform in a session. This prevents infinite loops and runaway reasoning chains.
- Operation-Based Limits (Operation Weighting): Limit based on what the agent is doing, not just how often.
For example:
- Reads → higher limits
- Writes → stricter limits
- Deletes / sensitive actions → extremely restricted
This ensures higher-risk operations are naturally harder to abuse.
Rate limiting per second / minute
Traditional rate limits control how many actions happen in a given time window. This still applies to AI—but you usually need faster feedback loops.
The key is persistent state plus deterministic logic: Using fast storage (like Redis or in-memory session state), you can track agent speed and behavior in near-real time. This allows you to slow or pause agents dynamically if they exceed safe thresholds.
This guarantees that only a safe number of actions occur within any given window.
Example: Actions-Per-Second (APS) Throttling
aps = agent_steps / (time.time() - start_time)
if aps > 5:
time.sleep(5)
else:
continueHow do I log and audit what AI does?
Comprehensive logging is essential for AI systems because you need to be able to understand and debug behavior that you didn't directly program. Here’s what you need:
- Structured logging: Every AI action should generate structured log entries with consistent fields: agent ID, operation, input, output, timestamp, duration, cost.
- Decision trails: Log not just what the AI did, but why it thought it should do it. This helps with debugging and improving agent behavior over time.
- Human-readable summaries: Raw logs are useful for debugging, but you also need human-readable summaries for business stakeholders. "Agent processed 47 customer inquiries, escalated 3 to human support, average resolution time 2.3 minutes."
Storing all of this in the database, or using a telemetry service, can help with observability and decision making.
How do I expose my backend to AI systems like MCP?
Model Context Protocol (MCP) is a standard for how AI systems interact with external tools and data sources. Supporting it means your backend can work with a wide range of AI platforms.
- Resource discovery: MCP clients need to discover what your backend can do. Implement resource and tool discovery endpoints that describe your capabilities in a machine-readable format. Use natural language to describe their utilities.
- Streaming support: AI interactions often benefit from streaming responses, especially for long-running operations or large data sets.
- Protocol compliance: Follow MCP specifications exactly. AI systems are less forgiving of protocol deviations than human-driven applications. Certain platforms (Xano is one of them) give you an easy-to-follow and easy-to-build environment for developing MCP Servers and tools to spec.
The bottom line
Here’s the reality: AI coding agents are powerful, unpredictable, and here to stay.
The goal isn't to control AI perfectly. AI will never be able to be controlled perfectly. It's to build a backend that can safely harness AI's capabilities while maintaining the reliability and security your users depend on. Xano can help—try it for free today.
