The tech world is buzzing about AI agents, yet industry giants like Apple and Amazon still face challenges implementing reliable AI features. Recent setbacks, such as Apple pulling AI News summarization tools due to hallucinations and Amazon’s struggles with Alexa, highlight a harsh reality: building trustworthy AI systems at scale is difficult. While flashy demos get many views online, creating production-ready solutions requires more than hype — it requires careful engineering.
Understanding workflows vs. agents
The term “AI agent” is often misused. According to Anthropic, a clear distinction exists between workflows and agents. Workflows are predefined code paths where large language models (LLMs) execute specific steps—like generating text or analyzing data — within a fixed sequence. These are reliable and manageable, ideal for most applications. True agents, however, dynamically decide their actions. They adapt in real-time, using tools and memory to achieve goals without step-by-step instructions. While agents promise flexibility, their unpredictability makes them prone to errors in real-world use.
If you prefer a visual breakdown, watch this video where I walk through real-world examples of these patterns in action.
Core building blocks for AI systems
The hype around “agents” creates confusion, pushing developers toward complex frameworks and tools. Just keep things simple. Start by working directly with the LLM API. Skip the abstractions. Build structured, testable workflows that solve real problems today. Effective AI systems (whether workflows or agents) rely on three common augmentations:
- Retrieval: Pulling relevant data from databases or APIs to inform the LLM’s responses (e.g., using a vector database for contextual information).
- Tools: Integrating external services (e.g., weather APIs or shipping trackers) to fetch real-time data.
- Memory: Storing past interactions to maintain context, much like ChatGPT’s conversation history.
Combining these elements already enhances basic LLM calls into more robust solutions.
Workflow patterns for reliability
Most problems don’t require agents. Start with these deterministic patterns:
- Prompt Chaining: Break tasks into sequential LLM calls. For example, drafting a blog post step-by-step—research, outline, and chapter writing—ensures control at each stage.
- Routing: Use LLMs to classify inputs and direct them to specialized workflows. A customer service AI might route refund requests and shipping inquiries to separate processes.
- Parallelization: Run independent LLM calls simultaneously. For instance, checking content for accuracy, safety, and prompt injections at the same time speeds up guardrails.
- Orchestrator-Worker: Assign an LLM to delegate subtasks. A customer email might trigger order lookups, policy checks, and API calls, with results synthesized into a final response.
- Evaluator-Optimizer: Use LLMs to critique and refine outputs. A blog post could be reviewed for clarity, revised based on feedback, and polished before publishing.
When to consider agentic patterns
True agents operate in loops: they plan, act, and adapt based on outcomes. For example, an AI software engineer like Devin attempts coding tasks, tests solutions, and iterates on errors. These systems, however, often fail unpredictably. True agents do have their place, but they’re still notoriously hard to scale. Focus on fundamentals first.
Practical tips for developers
- Avoid Overhyped Frameworks: Tools promising “easy AI agents” often abstract critical details. Build core components (retrieval, tools, memory) from directly around the API to retain control.
- Start Simple: Optimize single LLM calls before adding complexity. Use retrieval and prompt engineering to handle most cases.
- Invest in Testing: Create evaluation frameworks to measure changes objectively. Without metrics, improvements are just guesses.
- Implement Guardrails: Add checks before finalizing outputs. A secondary LLM can verify responses align with guidelines, preventing embarrassing mistakes.
- Scale Cautiously: Anticipate edge cases and hallucinations as user numbers grow. Gradually expand scope after validating core functionality.
Final thoughts
The allure of autonomous agents is strong, but most applications just need structured workflows. By isolating problems, iterating methodically, and prioritizing testing, developers can build AI systems that deliver consistent value — without the chaos.
P.S. If you’re a developer serious about AI engineering, let’s connect by subscribing to our newsletter. I share resources and tutorials to cut through the noise.
