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AI Agents – How to Build One That Actually Works
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קריאה 2 דק'
TL;DR: AI agents are trendy. Most don’t work in production. Here’s what we learned building them.

What AI Agents Are

LLM that uses tools autonomously.

Not just answering – taking actions.

Examples: search web, read files, send emails, execute code.

Types of Agents

Chain agents: fixed sequence.

ReAct: reason then act, iteratively.

Plan-and-execute: plan whole approach first.

Multi-agent: agents talking to each other.

Tool Definitions

Each tool: name, description, parameters.

Description quality determines LLM’s tool choice.

Vague description = wrong tool usage.

Be specific about when to use each tool.

Prompt Engineering for Agents

System prompt establishes role and constraints.

Available tools listed clearly.

Reasoning framework: think before acting.

Format for tool calls specified.

Handling Uncertainty

Agents make mistakes. Plan for it.

Retry logic with backoff.

Fallback to human when stuck.

Confidence thresholds – low confidence = escalate.

Cost Management

Agents can loop expensively.

Set max iterations per task.

Timeout per LLM call.

Budget per task.

Observability

Log every LLM call and tool use.

Trace whole agent execution.

Metrics: success rate, iterations, cost per task.

Tools: LangSmith, Langfuse, Weights & Biases.

Common Failure Modes

Getting stuck in loops.

Calling wrong tools.

Hallucinating tool results.

Losing track of goal.

Solution: constraints and validation at each step.

Production Considerations

Async execution for long tasks.

Job queue for reliability.

State persistence between runs.

Human-in-loop for critical decisions.

Testing Agents

Traditional unit tests don’t work well.

Evaluation datasets: expected outcomes for scenarios.

Regression testing when changing prompts.

Human review of edge cases.

Security

Agents doing actions = risks.

Whitelist allowed actions.

Confirm destructive actions.

Sandbox execution environments.

Rate limit tool usage.

Our Recommendation

Start simple: single tool, single action.

Add tools gradually.

Instrument heavily.

Human review before critical actions.

Expect 3-6 months to production-ready.

Based on Real Projects

This guide is based on our work with:

Further Reading

If this guide helped you, you might also want to read our comprehensive guide on AI Solutions.

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מחשבון פיתוח חכם

1. מה בונים?
אתר תדמית
חנות איקומרס
מערכת SaaS
אפליקציה
2. טכנולוגיה מועדפת
Vibe Coding (AI)
Custom Code
WordPress
Shopify
Wix / Webflow
React Native
3. שדרוגים
כתיבת תוכן
אוטומציות AI
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