Let’s be honest for a second. Most of what people call "AI" right now is just a fancy, glorified autocomplete. You give it a prompt, it spits out a wall of text, and then it sits there, waiting for you to tell it what to do next. It’s passive. It’s static. But that’s changing fast because of something called agentic design patterns.
If you’ve been following folks like Andrew Ng or the engineers over at LangChain, you know the vibe is shifting. We are moving away from "Zero-shot" prompting—where you pray the LLM gets it right the first time—and toward iterative loops. It’s the difference between asking a chef to cook a 5-course meal in one go without tasting anything, versus letting them prep, taste, adjust the salt, and serve when it’s actually ready.
Building these intelligent systems isn't just about picking a bigger model. It’s about the architecture. It’s about how you wrap that model in a workflow that allows it to reason, use tools, and, most importantly, fix its own mistakes.
The Reflection Pattern: Why AI Needs a Mirror
Have you ever hit "send" on an email and immediately realized you sounded like a jerk? That’s what happens to LLMs constantly. They just don't realize it until it's too late.
The Reflection pattern is probably the simplest of the agentic design patterns, but it’s insanely effective. You basically force the model to look at its own work and critique it. Think of it as a two-step dance. Step one: the model generates a draft. Step two: you prompt the same model (or a different one) to find the flaws in that draft.
It sounds redundant. It’s not.
In a real-world scenario—say, writing code—a reflection loop might look like this: the agent writes a Python script, then a "critic" agent looks for security vulnerabilities or PEP 8 violations. If it finds issues, it sends the code back with notes. The first agent then rewrites it. You’d be surprised how often this outperforms a single, massive prompt. It’s basically giving the AI a second thought. Sometimes, the best way to get a smart answer is to let the AI argue with itself for a minute.
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Tool Use and the Art of "Doing"
An LLM on its own is like a brain in a jar. It knows everything but can’t touch anything.
Tool use (sometimes called function calling) is the pattern that lets the brain grow hands. This is where you give the model a set of APIs it can call. Maybe it’s a calculator, a web search tool, or a connection to your company’s SQL database.
The "agentic" part comes in when the model decides when and how to use those tools. It’s not hardcoded. You don't tell it "use the search tool." Instead, you give it a goal, and it figures out that it can’t answer the question without checking the live price of Nvidia stock first.
But here’s the kicker: tool use is messy. Models hallucinate parameters. They try to use tools that don't exist. Expert-level agentic design requires robust error handling. If the model passes a string where an integer was expected, the system shouldn't just crash. It should feed the error message back to the model and say, "Hey, you messed up the data type. Try again." That’s a real intelligent system. It’s resilient.
Planning: The Architecture of Complex Goals
If you ask an agent to "Research the competitor's pricing, write a summary, and email it to the CEO," a basic model will likely trip over its own feet. It might try to do everything at once or skip steps.
The Planning pattern breaks this down.
Typically, this involves a "Planner" agent that decomposes a high-level goal into a sequence of sub-tasks. There are a few ways to do this. You’ve got the Chain of Thought approach, which is basically telling the model to "think step-by-step." Then you’ve got more advanced versions like Reasoning and Acting (ReAct), where the agent thinks, takes an action, observes the result, and then thinks again before the next step.
I’ve seen this go wrong when the plan is too rigid. A good agentic system needs to be able to pivot. If the "Research" step fails because a website is down, the planner should be able to re-route and try a different source rather than just giving up.
Multi-Agent Collaboration: The Digital Office
This is where things get really wild.
Instead of one giant agent trying to be a jack-of-all-trades, you build a team. You might have a "Researcher," a "Writer," and a "Manager." They talk to each other.
In frameworks like Microsoft’s AutoGen or CrewAI, these agents have specific roles and "backstories." This isn't just for flavor. It actually constrains the model’s persona, which leads to better results. A "Senior Security Engineer" agent will look at code very differently than a "Creative Copywriter" agent.
The challenge here is the "infinite loop." Agents can start talking in circles. "What do you think?" "I don't know, what do you think?" It happens. Preventing this requires a clear "orchestrator" or a set of termination conditions that stop the loop once a specific goal is met.
The Problem with Agentic Workflows (The Stuff Nobody Tells You)
Everyone loves to talk about how cool agents are, but nobody talks about the cost.
- Latency: Every time an agent "reflects" or "plans," you're waiting for another LLM call. If your workflow has 10 steps, your user is staring at a loading spinner for a long time.
- Cost: More calls = more tokens = more money. If you’re using GPT-4o or Claude 3.5 Sonnet, those iterative loops can get expensive fast.
- The "Vibes" Problem: It’s really hard to unit test an agent. It might work perfectly 9 times out of 10, and then on the 10th time, it gets stuck in a loop because it didn't like the way a website was formatted.
Building these systems is less like traditional software engineering and more like managing a group of very smart, very distracted interns. You have to be okay with a bit of unpredictability.
Real-World Implementation: Building Your First Agent
If you want to move beyond simple prompts, stop thinking about the prompt and start thinking about the state.
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An agentic system is basically a state machine. You need to track what has happened, what the current findings are, and what’s left to do.
Step 1: Define the Loop
Don't just ask for an answer. Build a loop where the model produces an output, a validator checks it, and if it fails, it goes back to the start. Use simple Python logic for the "if/else" part. You don't need an LLM to decide if a piece of code passed a unit test—use a real test runner.
Step 2: Give it Specialized Tools
Don't give your agent a "Google Search" tool if it only needs to look at your internal documentation. Keep the toolset small. The more tools you give an agent, the more likely it is to get confused and pick the wrong one.
Step 3: Monitor Everything
Use something like LangSmith or Arize Phoenix. You need to see the "trace" of the conversation. When the agent fails, you need to know exactly which step of the plan went sideways. Was it a bad plan? Or did the tool return bad data?
Actionable Next Steps for Developers
Ready to actually build? Here is the move:
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- Start with LangGraph or AutoGen: These libraries are designed specifically for these patterns. They handle the "state" so you don't have to build a complex database just to remember what the agent said two minutes ago.
- Implement a Reflection Step today: Take an existing LLM feature you have and just add a second call that asks the model to "Critique the above response for accuracy and tone." You will see an immediate jump in quality.
- Small Models for Small Tasks: Use a massive model (like GPT-4o) for the "Planning," but use a smaller, faster model (like Llama 3 or Haiku) for the "Reflection" or simple tool-calling tasks. It’ll save you a fortune.
Agentic design isn't about making AI "smarter" in terms of raw knowledge. It’s about giving it the agency to use that knowledge effectively. We're moving from a world of "Chatting" to a world of "Delegating." The people who figure out these patterns now are the ones who will build the truly "intelligent" systems of the next few years.