Building Production AI Agents

Everyone talks about building AI agents. But here's what nobody tells you: the hard part isn't getting an agent to work—it's getting it to work reliably, efficiently, and safely at scale. After building sia.build and working with hundreds of production agent deployments, I've learned that the real complexity lies in three fundamental areas: defining clear roles, equipping agents with the right tools, and ensuring they plan, execute, and course-correct effectively.

The Three Pillars of Agent Complexity

Building a production-ready AI agent isn't about throwing an LLM at a problem and hoping for the best. It requires careful orchestration across three critical dimensions:

1. Defining the Role

An agent without a clear role is like a developer without a spec—they'll build something, but probably not what you need. Role definition goes far beyond a simple system prompt. It encompasses:

At sia.build, we've found that agents with clearly defined roles complete tasks 40% faster and with 70% fewer errors compared to agents with vague instructions. The difference? Clarity reduces hallucination and keeps the agent focused on its core mission.

2. Defining the Tools

An agent is only as capable as its toolkit. But here's the catch: more tools don't equal better agents. In fact, we've observed that agents with too many tools suffer from "decision paralysis"—spending extra tokens deliberating which tool to use, often making suboptimal choices.

The solution? Context-aware tool injection. Instead of loading all tools upfront, sia.build uses a predictive system that injects tools dynamically based on:

• • The agent's current task phase
• • Historical tool usage patterns for similar tasks
• • Explicit dependencies (e.g., "git commit" requires "git add")
• • Security policies and user permissions

This reduces context usage by 70-80% while maintaining full agent capabilities. Tools appear exactly when needed, not before.

3. Planning, Execution, and Course Correction

Here's where most agent systems fail. They execute tasks reactively—taking one action at a time without a clear plan. This leads to:

But here's the real problem: making a plan is easy. Making agents stick to the plan is hard.

The Solution: Execution Tracking Like Todo Management

At sia.build, we treat agent execution like a developer managing a todo list. Each step in the plan becomes a tracked item with a status: pending, in_progress, completed, or failed.

This sounds simple, but it's the difference between agents that seem to work in demos and agents that actually work in production.

// Agent workflow with plan tracking and deviation detection
async function executeTask(task: Task) {
  // Phase 1: Planning
  const plan = await agent.createPlan(task);
  // Returns: Step-by-step execution plan with dependencies

  const tracker = new PlanTracker(plan);

  // Phase 2: Execution with strict tracking
  for (const step of plan.steps) {
    // Mark current step as in-progress
    tracker.markInProgress(step.id);

    const result = await agent.executeStep(step, {
      // Pass allowed tools for this step only
      allowedTools: step.requiredTools,

      // Deviation callback: triggered if agent tries unauthorized tool
      onDeviation: async (attemptedTool) => {
        console.warn(`Deviation detected: agent tried to use ${attemptedTool}`);

        // Stop execution and demand explanation
        const explanation = await agent.explainDeviation(step, attemptedTool);

        if (!explanation.justified) {
          throw new Error('Unauthorized deviation from plan');
        }

        // Update plan if deviation is justified
        plan = await agent.updatePlan(plan, explanation);
        tracker.updatePlan(plan);
      }
    });

    // Verify step actually completed what it claimed
    const verified = await verifyStepCompletion(step, result);

    if (!verified.success) {
      tracker.markFailed(step.id, verified.reason);
      throw new Error(`Step verification failed: ${verified.reason}`);
    }

    // Record for potential rollback
    await agent.recordRevision(step, result);
    tracker.markCompleted(step.id);
  }

  // Phase 3: Final verification
  const verification = await agent.verifyOutcome(task, plan);

  if (!verification.passed) {
    // Rollback to last good state
    await agent.revertToRevision(verification.lastGoodRevision);
  }

  return {
    success: true,
    plan,
    tracker: tracker.getHistory(),
    revisions: agent.getRevisionHistory()
  };
}

// Verify step completion (not just trust agent's word)
async function verifyStepCompletion(step: Step, result: any) {
  switch (step.type) {
    case 'file_create':
      // Actually check if file exists
      return { success: fs.existsSync(result.filePath) };

    case 'api_call':
      // Verify response code
      return { success: result.statusCode === 200 };

    case 'database_write':
      // Query database to confirm write
      const record = await db.query(result.id);
      return { success: record !== null };

    default:
      return { success: true };
  }
}

Beyond Step Optimization: Quality and Speed

Everyone optimizes for reducing the number of steps. Fewer API calls = lower cost, right? But that's optimizing the wrong metric. What matters is:

Safe Guardrails: The Non-Negotiable Requirement

Here's a hard truth: AI agents will make mistakes. They'll misinterpret instructions, choose wrong tools, or execute actions with unintended consequences. The question isn't if this will happen, butwhen—and what systems you have in place to prevent catastrophe.

These aren't edge cases. They're inevitable outcomes when you give autonomous systems powerful tools. sia.build implements multiple layers of guardrails:

Pre-Execution Guardrails

• • Static analysis: Detect dangerous patterns before execution (e.g., recursive deletes, force pushes)
• • Permission checks: Verify agent has explicit permission for sensitive operations
• • Cost estimation: Predict and cap execution costs before starting
• • Dry-run mode: Simulate actions without side effects for high-risk operations

Runtime Guardrails

• • Rate limiting: Cap API calls per minute/hour to prevent runaway costs
• • Circuit breakers: Automatically halt execution on repeated failures
• • Resource monitoring: Track CPU, memory, network usage in real-time
• • Anomaly detection: Flag unusual patterns (sudden spike in tool usage, etc.)

Post-Execution Guardrails

• • Outcome verification: Validate results match expected criteria
• • Security scanning: Check for credential leaks, injection vulnerabilities
• • Rollback capabilities: Revert changes if verification fails
• • Audit logging: Comprehensive logs for compliance and debugging

Iteration and Revision: The Killer Feature

Here's what separates production-grade agents from demos: the ability to iterate and revert. Developers need to:

sia.build treats agent execution like git treats code: every execution is a commit, every change is tracked, and every state is recoverable. This isn't just convenient—it's essential for production deployments.

// Revision system in action
const execution = await sia.execute({
  agentId: 'code-reviewer',
  task: 'Review PR #123',
  config: { strictMode: true }
});

// Later: something went wrong, revert
await sia.revertToRevision(execution.revisionId - 1);

// Or: try with different config
const retry = await sia.reExecute(execution.id, {
  config: { strictMode: false, autoFix: true }
});

// Compare outcomes
const diff = await sia.compareRevisions(
  execution.revisionId,
  retry.revisionId
);

console.log('Changes:', diff.changedFiles);
console.log('Improvement:', diff.qualityScore);

sia.build: Agents That Plan, Execute, and Recover

We built sia.build to solve these exact problems. Our platform enables developers to create agents that:

Getting Started

Ready to build agents that actually work in production? Here's how to get started with sia.build:

// Install the SDK
npm install @sia-build/sdk

// Define your agent
import { SiaClient } from '@sia-build/sdk';

const sia = new SiaClient({ apiKey: process.env.SIA_API_KEY });

const agent = await sia.agents.create({
  name: 'Code Reviewer',
  role: {
    identity: 'Senior code reviewer with expertise in TypeScript',
    scope: ['code-review', 'static-analysis', 'suggestions'],
    boundaries: ['no-destructive-changes', 'no-auto-merge'],
    successCriteria: {
      mustFindCriticalIssues: true,
      maxReviewTime: '5 minutes'
    }
  },
  tools: {
    injection: 'dynamic', // Tools loaded on-demand
    allowList: ['file-read', 'git-diff', 'lint', 'test']
  },
  guardrails: {
    preExecution: ['permission-check', 'cost-estimate'],
    runtime: ['rate-limit', 'circuit-breaker'],
    postExecution: ['outcome-verify', 'security-scan']
  },
  planning: {
    enabled: true,
    maxSteps: 20,
    allowRevision: true
  }
});

// Execute with full tracking
const execution = await sia.execute({
  agentId: agent.id,
  task: 'Review PR #456',
  tracking: true
});

// Monitor progress
console.log('Plan:', execution.plan);
console.log('Current step:', execution.currentStep);
console.log('Revision:', execution.revisionId);

The Future of Agent Development

AI agents are moving from demos to production. The teams that succeed won't be the ones with the fanciest models or the most tools—they'll be the ones with robust planning, strong guardrails, and the ability to iterate and recover when things go wrong.

At sia.build, we're focused on making these capabilities accessible to every developer. Because the future of software isn't just about what agents can do—it's about what they can do reliably, safely, and at scale.