Elegant Method Interception in AI

Runtime method interception is the difference between an AI agent framework that scales gracefully and one that collapses under the weight of its own customizations. When every tool call, every API request, and every file write passes through a clean interception layer, you gain observability, safety, and composability without ever modifying a single line of tool source code.

This matters because AI agent ecosystems are growing fast. Claude Code alone exposes over 40 internal tools, and skill authors need ways to extend, observe, and guard those tools without forking the runtime. The answer is method interception done right.

Key Takeaways

Proxy-based interception wraps tool calls transparently, preserving the original interface while adding behavior
Decorator patterns let skill authors stack multiple concerns (logging, validation, retry) on a single tool
Hook systems decouple interception logic from tool implementation, enabling community-driven extensions
Runtime guards prevent dangerous operations by intercepting calls before they execute, not after
Composition over mutation keeps agent architectures maintainable as tool counts scale past 100

What Is Method Interception in the AI Context?

In traditional software, method interception means inserting logic before, after, or around a function call without modifying the function itself. Aspect-oriented programming, middleware chains, and event hooks are all forms of this pattern.

In AI agent systems, the concept maps directly to tool calls. When an AI agent decides to read a file, execute a shell command, or query a database, that decision triggers a tool invocation. Method interception gives you a clean insertion point to observe what the agent intends to do, validate it, modify it, or block it entirely.

The Claude Code hook system is a production example. PreToolUse and PostToolUse hooks let skill authors intercept any tool invocation without touching the tool's implementation.

The Five Interception Patterns

1. The Proxy Pattern

The proxy pattern wraps a tool behind an identical interface. The agent doesn't know it's talking to a proxy instead of the real tool.

function createToolProxy(originalTool, interceptor) {
  return new Proxy(originalTool, {
    apply(target, thisArg, args) {
      const context = { tool: target.name, args, timestamp: Date.now() }
      const decision = interceptor.beforeCall(context)
      if (decision.blocked) return decision.fallbackResult
      const result = Reflect.apply(target, thisArg, decision.modifiedArgs || args)
      interceptor.afterCall(context, result)
      return result
    }
  })
}

This pattern works well when you need transparent interception. The agent's behavior doesn't change. The tool's interface doesn't change. Only the plumbing between them gains new capability.

2. The Decorator Stack

Decorators are composable. You can stack multiple concerns on a single tool without creating a tangled dependency graph.

const enhancedTool = withRetry(
  withLogging(
    withValidation(
      originalTool,
      { maxInputLength: 10000 }
    ),
    { level: 'debug' }
  ),
  { maxAttempts: 3 }
)

Each decorator handles one concern. Logging doesn't know about validation. Validation doesn't know about retry. They compose cleanly because each one preserves the tool's interface contract.

3. The Event Hook System

Hooks decouple interception logic from the interception point. Instead of wrapping a tool directly, you register handlers for events that fire when tools are invoked.

This is the model Claude Code uses. A skill registers a PreToolUse hook, and the runtime calls that hook before every matching tool invocation. The hook can approve, modify, or reject the call.

The advantage is discoverability. A team can inspect which hooks are registered, what they do, and in what order they fire. With direct proxies, you need to trace the proxy chain to understand behavior.

4. The Middleware Pipeline

Borrowed from web frameworks like Express and Koa, middleware pipelines pass tool invocations through an ordered chain of handlers. Each handler can modify the request, modify the response, or short-circuit the chain.

const pipeline = createPipeline([
  auditMiddleware,      // logs every call
  rateLimitMiddleware,  // enforces call frequency
  sandboxMiddleware,    // restricts filesystem access
  executionMiddleware   // actually runs the tool
])

Order matters. Audit runs first so it sees every call, even blocked ones. Rate limiting runs before sandboxing so rejected calls don't consume sandbox resources.

5. The Aspect-Oriented Approach

Aspects define cross-cutting concerns declaratively. Instead of writing interception code, you declare what should happen and let the framework weave it in.

aspects:
  - pointcut: "tool.execute(Bash)"
    before: validateCommand
    after: logResult
    onError: notifyAdmin
  - pointcut: "tool.execute(Write)"
    before: [checkPermissions, validatePath]
    after: updateFileIndex

This pattern scales well when you have many tools and many concerns. The configuration is readable, auditable, and easy to modify without touching code.

How Do These Patterns Apply to AI Skills?

AI skills are essentially instruction sets that modify agent behavior. When a skill needs to intercept tool calls, it faces a design choice: should the interception be implicit (baked into the skill's instructions) or explicit (registered as a hook or middleware)?

The answer depends on the trust model. For skills distributed through a marketplace like aiskill.market, explicit interception via hooks is safer because it's auditable. Platform operators can inspect what a skill intercepts and why.

For internal team skills, implicit interception through prompt engineering can be simpler. The skill's instructions tell the agent to check certain conditions before using certain tools. No hook registration needed, but also no formal audit trail.

The emerging best practice is a hybrid approach. Use hooks for safety-critical interception (blocking dangerous commands, enforcing file access rules) and prompt-based interception for behavioral guidance (preferring certain tools over others, formatting output in specific ways).

What Makes Interception "Elegant"?

Elegance in method interception comes down to three properties.

Transparency. The intercepted tool behaves identically from the agent's perspective unless the interceptor explicitly changes something. No surprise side effects, no hidden state mutations.

Composability. Multiple interceptors can coexist without interfering with each other. Adding a new interceptor doesn't require modifying existing ones.

Reversibility. Any interceptor can be removed without breaking the system. The tool works fine without it. This property is critical for debugging, since you can disable interceptors one by one to isolate issues.

When all three properties hold, you get an architecture that grows gracefully. New tools can be added without updating interceptors. New interceptors can be added without updating tools. The system remains understandable even as it scales.

Performance Considerations

Interception adds overhead. Every tool call now passes through one or more layers of logic before reaching the actual implementation. In AI agent systems, this overhead is usually negligible compared to the latency of the LLM calls that trigger tool invocations, but it matters at scale.

Three strategies keep interception fast. First, lazy initialization: don't set up interceptors until they're actually needed. Second, short-circuit evaluation: if an interceptor determines early that it doesn't apply, exit immediately without running the full logic. Third, async-friendly design: interceptors that do I/O (like logging to a remote service) should never block the tool call.

For AI skills that run in production environments, profiling interception overhead is worth the effort. A well-designed interceptor adds microseconds. A poorly designed one adds milliseconds per tool call, and those milliseconds compound when the agent makes hundreds of calls per session.

Real-World Examples

Security scanning. A PreToolUse interceptor on the Bash tool that checks every command against a deny list of dangerous patterns. The interceptor runs in under a millisecond and prevents catastrophic mistakes like rm -rf / or credential exfiltration.

Usage analytics. A PostToolUse interceptor that logs tool name, duration, and success/failure to a metrics service. This powers dashboards showing which tools agents use most and where they fail.

Cost control. A middleware that tracks cumulative API calls and halts agent execution when costs exceed a threshold. This prevents runaway agents from generating unexpected bills.

Format enforcement. A decorator on the Write tool that validates output format before writing. If an agent is supposed to produce JSON, the decorator catches malformed output before it hits disk.

FAQ

Can method interception slow down AI agents significantly?

In practice, no. LLM inference takes seconds per call. Well-designed interceptors add microseconds. The bottleneck is always the model, not the interception layer. Problems only arise with interceptors that make synchronous network calls or perform heavy computation.

How does interception differ from prompt engineering?

Prompt engineering tells the agent what to do. Interception enforces what the agent can do. Prompts are suggestions the model might ignore. Interceptors are code the model cannot bypass. Both have roles, but interception provides harder guarantees.

Which pattern should I start with for a new AI skill?

Start with the hook system if your platform supports it (Claude Code does). Hooks are the simplest to implement, the easiest to debug, and the most portable across skill versions. Graduate to middleware pipelines when you need ordered composition of multiple concerns.

Is runtime interception safe for production AI agents?

Yes, if interceptors follow the transparency and reversibility principles. The risk comes from interceptors that silently modify tool inputs or outputs in ways that confuse the agent. Keep interceptors observable: log what they change and why.

Sources

Explore production-ready AI skills at aiskill.market/browse or submit your own skill to the marketplace.