# Your AI Agent Has Too Much Power

> Source: <https://dev.to/thienban/your-ai-agent-has-too-much-power-2bah>
> Published: 2026-09-23 19:39:37+00:00

If you've connected an LLM to tool calling, APIs, or database queries recently, you know the feeling: **agents are terrifyingly unpredictable.**

Giving an LLM the ability to decide its own next step means shifting from passive text generation to **active system execution**. In demos, it looks like magic: the agent reasons, picks a tool, fetches data, and loops until the task is solved.

In real life?

The traditional answer to this is the classic **LLMOps sledgehammer**: spin up Docker containers for Langfuse, Helicone, OpenLit, a vector database, ClickHouse, Redis, and an S3 bucket just to trace calls and apply basic guardrails.

For most developers and lean teams, **the monitoring infrastructure ends up costing more in maintenance and compute than the LLM calls themselves.**

There is a better way: handling security, guardrails, and cost controls **in-process**, directly at the application runtime level.

When building autonomous workflows or multi-step agents, security vulnerabilities aren't theoretical — they fall into three clear failure modes:

```
[User / External Input]
         │
         ▼
 ┌───────────────┐
 │   Prompt /    │  <-- 1. Prompt Injection / Malicious Context
 │ Ingested Data │
 └───────┬───────┘
         ▼
 ┌───────────────┐
 │  LLM Engine   │  <-- 2. Token Loop & Budget Drainage (Hallucination)
 └───────┬───────┘
         ▼
 ┌───────────────┐
 │ Tool Execution│  <-- 3. Unauthorized Side-Effects / PII Exfiltration
 └───────────────┘
```

Meet AG-Men (avantGate's in-process engine): a tiny, sub-millisecond guardian that sits directly between your model's thoughts and your backend APIs.

**[Avant Gate](https://github.com/thienban/avantGate)** runs directly in your TypeScript process.

Imagine a user submits an invoice query packed with an indirect prompt injection and a customer's raw credit card. Here is how the AG-Men defend your runtime step by step:

`529 Overloaded`. The Shapeshifter catches the fall mid-air and swaps to a backup provider, landing the mission safely without a glitch.
Here is how you wrap your agent runtime with `avantGate` in TypeScript:

``` js
import { AvantGate } from 'avantgate';

const gate = new AvantGate({
  budget: {
    maxCostPerMinute: 0.50, // Circuit breaker: halts calls if threshold is reached
    maxTokensPerTurn: 4000
  },
  pii: {
    redact: ['email', 'credit-card'],
    maskChar: '*'
  },
  fallback: [
    'anthropic/claude-3-5-sonnet',
    'openai/gpt-4o-mini'
  ]
});
```

In an agentic workflow, you must never let the model invoke tools with unchecked parameters. You can intercept and validate both the step input and the agent's intent:

``` js
import { z } from 'zod';

// Define strict contract for the tool
const SearchDatabaseSchema = z.object({
  query: z.string().max(200),
  limit: z.number().int().min(1).max(20),
});

async function runSecuredAgentStep(userPrompt: string) {
  // 1. AvantGate sanitizes input (PII redaction) & checks budget in-process
  const safeExecution = await gate.run(async (context) => {

    const response = await context.completion({
      model: 'anthropic/claude-3-5-sonnet',
      messages: [{ role: 'user', content: userPrompt }],
      tools: [
        {
          name: 'search_database',
          description: 'Search internal records',
          parameters: SearchDatabaseSchema,
        },
      ],
    });

    // 2. Validate agent tool calls before execution
    if (response.toolCalls) {
      for (const call of response.toolCalls) {
        if (call.name === 'search_database') {
          const validatedArgs = SearchDatabaseSchema.parse(call.args);
          return await executeInternalSearch(validatedArgs);
        }
      }
    }

    return response.content;
  });

  return safeExecution;
}
```

If an injection attempts to inject `DROP TABLE` or trigger a recursive chain that blows past your token limits, `avantGate` halts the execution context before it wrecks your infrastructure.

| Concern | External LLMOps Stack | In-Process Control Plane (avantGate) | 
|---|---|---|
| **Infra overhead** | Docker, Postgres, ClickHouse, Redis | **Zero** (Pure TypeScript module) | 
| **Network Latency** | Additional network hops per call | **Sub-millisecond** (runs in memory) | 
| **Data Privacy** | Prompts/logs stored in external databases | **Zero egress** (data never leaves your runtime) | 
| **Maintenance** | Migrations, backups, version upgrades | **Update** avantGate | 

Agents are the future of software engineering, but building agents without deterministic boundaries is an invitation for disaster. You don't need a 5-container infrastructure stack to build secure, cost-contained AI tools.

Check out the project and docs on GitHub:

👉 [Avant Gate](https://github.com/thienban/avantGate)