Building Reliable AI Agents: Architecture, Challenges, and Lessons Learned

AI agents are everywhere right now. Every week brings a new framework, a new benchmark, and a new demo of an LLM autonomously browsing the web, writing code, or booking a flight. Most of these demos are impressive. Most of them also fall apart the moment you try to use them for something real.

This post is about what it actually takes to build AI agents that hold together beyond the demo — drawing from my work on agentic systems for software environments. I'll cover the core architecture patterns, the failure modes nobody talks about in blog posts, and the design decisions that actually matter in practice.

What Is an AI Agent, Really?

Before getting into architecture, it's worth being precise about what we mean. The term "agent" is used so loosely that it has become nearly meaningless.

For the purposes of this post, an AI agent is a system that:

1. Receives an objective (not just a single prompt)
2. Plans and executes a sequence of actions to achieve it
3. Observes the results of those actions
4. Adapts based on what it observes
5. Does this autonomously, without requiring human input at every step

The key word is autonomously. A chatbot that answers questions is not an agent. A system that writes code, runs it, reads the error, fixes the code, and runs it again — that's an agent.

The Core Loop

Every agent, regardless of how complex it becomes, is built around some version of this loop:

Observe → Think → Act → Observe → ...

In code, the simplest possible version looks something like this:

def run_agent(objective: str, tools: list[Tool], max_steps: int = 20):
    memory = []

    for step in range(max_steps):
        context = build_context(objective, memory)
        decision = llm.complete(context)
        action = parse_action(decision)

        if action.type == "finish":
            return action.result

        observation = action.tool.call(action.args)
        memory.append({"action": action, "observation": observation})

    raise MaxStepsExceeded("Agent did not finish within the step limit")

This is the ReAct pattern — Reason + Act — and it's the foundation of most production agent systems today.

The Tool Layer

An agent's capabilities are defined entirely by its tools. The LLM can reason about anything, but it can only do what its tools allow.

A well-designed tool interface looks like this:

from dataclasses import dataclass

@dataclass
class Tool:
    name: str
    description: str
    parameters: dict  # JSON Schema

    def call(self, **kwargs) -> str:
        raise NotImplementedError

    def to_llm_spec(self) -> dict:
        return {
            "name": self.name,
            "description": self.description,
            "parameters": self.parameters,
        }

A few things matter enormously here:

The description is your most important parameter. The LLM decides which tool to use based on the description. Vague descriptions lead to wrong tool selection. Be specific about what the tool does, what inputs it expects, and what it does not do.

Return strings, not structured data. The observation fed back into the LLM context is text. A well-formatted string that directly answers the implicit question the agent was trying to answer leads to faster, more reliable reasoning.

Handle errors gracefully. Tools will fail. The agent needs to see the error message and have a chance to recover — not crash.

class FileReadTool(Tool):
    name = "read_file"
    description = (
        "Read the contents of a file. Returns the full text content. "
        "Use this when you need to examine an existing file. "
        "Do not use this to check whether a file exists — use list_directory instead."
    )

    def call(self, path: str) -> str:
        try:
            with open(path, "r") as f:
                return f"Contents of {path}:\n\n{f.read()}"
        except FileNotFoundError:
            return f"Error: File '{path}' does not exist."
        except PermissionError:
            return f"Error: Permission denied when reading '{path}'."

Memory and Context Management

The biggest practical challenge in agent development is context management. LLMs have finite context windows, and a long-running agent will exhaust them.

There are three broad approaches:

1. Full History (Simple, Doesn't Scale)

Keep everything in the context. Works fine for short tasks. Falls apart for anything involving more than a few dozen steps.

2. Sliding Window

Keep only the last N steps. Simple to implement, but the agent can "forget" information from earlier in the task that turns out to be important.

3. Structured Memory

Maintain a separate memory store with explicit read/write operations. The agent stores important facts, retrieves them when needed, and the raw step-by-step history gets compressed or discarded.

class AgentMemory:
    def __init__(self):
        self.working_memory: list[dict] = []
        self.long_term: dict[str, str] = {}

    def add_step(self, action, observation):
        self.working_memory.append({"action": action, "observation": observation})
        if len(self.working_memory) > 20:
            self.working_memory = self.working_memory[-20:]

    def remember(self, key: str, value: str):
        self.long_term[key] = value

    def to_context(self) -> str:
        parts = []
        if self.long_term:
            parts.append("Key facts:\n" + "\n".join(
                f"- {k}: {v}" for k, v in self.long_term.items()
            ))
        parts.append("Recent steps:\n" + format_steps(self.working_memory))
        return "\n\n".join(parts)

The Failure Modes Nobody Mentions

Most agent tutorials show you the happy path. Here's what actually goes wrong.

Hallucinated Tool Calls

The LLM will occasionally call a tool with arguments that look plausible but are subtly wrong — a file path that doesn't exist, a parameter name off by one character, a value in the wrong format.

Mitigation: Validate tool inputs before execution. Return a descriptive error that tells the agent exactly what was wrong.

Getting Stuck in Loops

Without explicit loop detection, an agent will sometimes repeat the same action five times because the observation doesn't contain what it expected and it doesn't know how to proceed.

Mitigation: Track action history. If the last N actions are identical, force the agent to reconsider.

def detect_loop(memory: list[dict], window: int = 3) -> bool:
    if len(memory) < window:
        return False
    recent = [m["action"].name for m in memory[-window:]]
    return len(set(recent)) == 1

Context Poisoning

Early in the task, the agent makes a wrong assumption and records it in memory. Everything that follows is built on that assumption. The error compounds.

Mitigation: Build in explicit reflection steps. Periodically ask the agent to review its assumptions against the observations it has collected.

Scope Creep

Given a vague objective, an agent will sometimes interpret it as broadly as possible — deleting files it wasn't asked to touch, making API calls with side effects, modifying state that should have been left alone.

Mitigation: Define explicit boundaries in the system prompt. Use sandboxed tool implementations. For anything irreversible, require confirmation.

What Actually Makes an Agent Reliable

After spending significant time building and debugging these systems, the things that matter most are not what I expected.

Prompt engineering matters more than architecture. The clearest improvements came from better system prompts — more precise tool descriptions, clearer task framing, explicit instructions about handling uncertainty. Not from switching frameworks.

Simpler is more robust. A flat ReAct loop with good tools and a well-written system prompt outperforms complex multi-agent architectures on most tasks.

Failure recovery beats failure prevention. You cannot write prompts that prevent all errors. Build systems that detect when something went wrong and know how to recover.

Test with adversarial inputs. The tasks where agents fail most dramatically are edge cases and ambiguous objectives. Your evaluation suite should include these, not just clean happy-path scenarios.

Where This Is Going

The current generation of AI agents is impressive but brittle. They work well on well-defined tasks in constrained environments, and fail unpredictably when those constraints are violated.

The interesting open problems are around reliability at scale: how do you build an agent that completes complex, long-horizon tasks consistently? How do you make autonomous systems safe enough to trust with consequential actions?

These are not just engineering problems. They involve how to specify objectives precisely, how to build systems that know what they don't know, and how to maintain human oversight over systems that act faster than humans can review.

That's what I'm working on. I'll keep writing about it here as I learn more.