The more I read about AI agents, the more a pattern starts to emerge. Different papers, frameworks, and prototypes all describe them in different ways — yet underneath, the architecture feels strangely familiar. These systems can plan, reason, and act through APIs or tools. They don’t just respond; they do. And as I tried to understand how they actually work, I realized something that helped it all click for me: Building an AI agent isn’t that different from how a compiler or interpreter works.
That analogy isn’t new or revolutionary, but it gave me a mental model I could finally hold onto. It turns a fuzzy idea into something structured — something engineers can reason about. Here’s the five-step pattern I keep noticing, and why it helps me make sense of how agentic systems really function.
1. Define the World (The Toolset)
Every agent operates in a world — a limited one. Before anything can happen, it needs to know what it can do. That means defining the tools or capabilities available to it — APIs, databases, or external services. Each of these is described in a small schema: what the tool does (book_flight) and what it needs (origin, destination, date). It reminds me of how compilers rely on header files and libraries to know what functions exist. Defining the world gives the agent its vocabulary — its sense of boundaries.
2. Parse Intent into a Plan (NLP → DAG)
Once the world is defined, the next challenge is turning human intent into something executable. When someone says, “Book my work trip to Berlin next week,” the agent (or the model behind it) breaks that down into a plan:
CheckBudget → SearchFlights → ReserveHotel → SendConfirmation
That’s essentially syntactic and semantic analysis — not literal parsing like a compiler would do, but the same spirit of translation: turning free-form input into structured logic. The model parses natural language into a structured workflow — often a Directed Acyclic Graph (DAG) of actions.
This is the part that frameworks like LangChain, OpenAI function-calling, or ReAct build around — giving the model a way to reason in structured steps rather than guess in free text. I found this perspective freeing: it’s not “AI magic,” it’s engineering — converting words into plans.
3. Validate the Plan (Guardrails & Safety)
This stage keeps the system honest. Before any action runs, the generated plan is checked against the defined tool schemas. If a tool call is missing required inputs, or a parameter is invalid, the process stops right there. That’s the agent’s type checker — its way of making sure the plan is structurally and logically sound before touching the real world.
In practice, this is where most real-world failures occur: JSON output missing keys, invalid parameter types, or unauthorized API calls. So validation isn’t optional — it’s the difference between experimentation and reliability.
4. Execute the DAG (Runtime Execution)
Once the plan passes validation, the execution phase begins. Each tool runs in order — sometimes in parallel, depending on dependencies — passing outputs downstream like function calls in a larger program.
In compiler terms, this is the runtime. In agentic systems, it’s the Executor that manages this flow — the heartbeat that keeps Action → Observation → Reason → Action looping until the goal is met. When you think of agents this way, autonomy feels less mystical — it’s just well-orchestrated flow control.
5. Monitor & Report Status (Async Orchestration)
Finally, real workflows take time — and agents aren’t meant to block you. The last step is simple but elegant: return a job_id when the task starts, let the user check progress, and only return results once everything’s done. It’s the same pattern we see in distributed systems, build pipelines, or even compilers running large projects. It’s about keeping the system responsive, traceable, and observable.
Intent → Plan → Validate → Execute → Monitor
Putting It All Together
The more I read about agents, the more this five-step structure shows up — not always explicitly, but quietly guiding how things work. Each stage — defining, parsing, validating, executing, monitoring — turns what feels like an opaque black box into a familiar engineering pipeline. Of course, real agents include additional layers: context management, memory, feedback loops, and sometimes even collaboration across multiple agents. But beneath all that, this structure remains — a kind of backbone everything else builds on.
That’s what helped me understand it: we’re not building mystical systems; we’re rediscovering structured ones. Just with a new compiler — one that turns context into action instead of code into instructions.
What’s Next
This is Part 1 of a small, ongoing series:
- First Principles — this post
- Prototype — building a tiny example that turns language into executable JSON
- Orchestration — how multiple agents coordinate into larger systems
I’m still connecting the dots, but this framework has made the space a lot clearer to me. If you’ve been exploring agents too, I’d love to hear what patterns you’ve started to notice.
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