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How to Build Reliable AI Agents for Business Applications

July 8, 2025•Sean Falconer•Original Link•2 minutes
AI Agents
LLMs
Enterprise AI

Learn how to design AI agents with clear rules and predictable outcomes for enterprise use, avoiding the pitfalls of unreliable chatbots.

The Problem with General-Purpose AI

Large language models (LLMs) like GPT and Claude are powerful but fundamentally unreliable, with hallucination rates as high as 88% in some scenarios (Stanford study). Businesses need AI systems that operate with clear rules, defined behavior, and predictable outcomes—not open-ended chatbots prone to errors.

The purpose-built model training pipeline

Key Principles for Reliable AI

  1. Closed-World Problems: Focus on bounded tasks with measurable outcomes (e.g., processing insurance claims, troubleshooting IT tickets). These are easier to automate and trust.
  2. Purpose-Built Agents: Design AI systems for specific jobs (e.g., triaging support tickets, generating reports) rather than general intelligence.
  3. Scoped Tools: Provide agents with strongly typed, constrained tools (e.g., "fetch today’s unshipped orders" instead of raw SQL access).

Building purpose-built agents

Why Context Matters

LLMs don’t inherently understand your business. Unlike traditional models trained on proprietary data, foundation models require explicit context for each interaction. Reliability must be engineered through:

  • Tightly scoped prompts
  • Layered governance
  • Human-in-the-loop oversight

The Future of Enterprise AI

Andrej Karpathy, former Tesla AI director, compares LLMs to operating systems. The key is treating AI like software: modular, composable, and designed for control. By prioritizing closed-world problems and purpose-built agents, businesses can deploy AI that delivers consistent results—not surprises.

For more on AI agents, read AI Agents: Key Concepts and How They Overcome LLM Limitations.

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August 15, 2025•Malaya Rout

Agentic AI vs AI Agents Key Differences and Future Trends

Explore the distinctions between Agentic AI and AI agents, their advantages, disadvantages, and the future of multi-agent systems.

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About the Author

David Chen

David Chen

AI Startup Analyst

Senior analyst focusing on AI startup ecosystem with 11 years of venture capital and startup analysis experience. Former member of Sequoia Capital AI investment team, now independent analyst writing AI startup and investment analysis articles for Forbes, Harvard Business Review and other publications.

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Startup Analysis
Venture Capital
Market Research
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Experience
11 years
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