Building Reliable LLM Agents in 2025 – Best Practices, Code, and Flowcharts

Large-language-model (LLM) agents represent the next evolution of AI systems. Unlike simple chatbots, agents reason, call tools and APIs, manage memory and context, and make decisions based on feedback loops. Because they integrate with sensitive data and take actions in the real world, reliability is paramount.

In this post, you'll learn how to design, implement, and test reliable LLM agents by combining insights from industry leaders (UiPath, Vellum, Anthropic, Evidently AI, LogRocket), current security guidelines, and hands-on coding examples.

🧩 What Makes an LLM Agent?

An LLM agent wraps a language model in a loop that iteratively plans tasks, invokes external tools, and updates state. A typical agent pipeline looks like this:

⚙️ 1. Design Agents That Fail Safe (Not Just Fast)

Start with single-responsibility agents and modularize logic.
For deterministic tasks (e.g., math, date parsing), delegate to APIs instead of the model.

Avoid blind retries—LLMs are non-deterministic. Instead, handle errors inside the tool or workflow and surface structured exceptions.

Before you prompt, define:

• Objectives & KPIs
• Output format & correctness criteria
• Evaluation metrics (accuracy, reasoning, latency)

🧠 2. Control Context and Memory

LLM agents have limited context windows. Optimize memory usage for reliability and cost.

Only carry critical variables between steps and summarize everything else. Use retrieval over raw log dumps.

🧰 3. Treat Every Capability as a Tool

Each external action (API, database, computation) should be treated as a tool with strict input/output contracts.

Example: Function Calling with OpenAI API

import openai
import requests

openai.api_key = "YOUR_API_KEY"

functions = [
    {
        "name": "get_weather",
        "description": "Fetch current weather data for a city.",
        "parameters": {
            "type": "object",
            "properties": {"location": {"type": "string"}},
            "required": ["location"]
        },
    }
]

def get_weather(location: str) -> str:
    try:
        resp = requests.get(
            "https://api.openweathermap.org/data/2.5/weather",
            params={"q": location, "units": "metric", "appid": "YOUR_OWM_KEY"},
            timeout=5
        )
        data = resp.json()
        temp = data["main"]["temp"]
        desc = data["weather"][0]["description"]
        return f"It is {temp:.1f}°C and {desc} in {location}."
    except Exception as e:
        return f"Error fetching weather: {e}"

messages = [{"role": "user", "content": "What's the weather in Paris?"}]
response = openai.ChatCompletion.create(
    model="gpt-4o",
    messages=messages,
    functions=functions,
)

choice = response["choices"][0]
if "function_call" in choice["message"]:
    import json
    fn_name = choice["message"]["function_call"]["name"]
    args = json.loads(choice["message"]["function_call"]["arguments"])
    if fn_name == "get_weather":
        result = get_weather(**args)
        followup = openai.ChatCompletion.create(
            model="gpt-4o",
            messages=messages + [
                choice["message"],
                {"role": "function", "name": fn_name, "content": result},
            ],
        )
        print(followup["choices"][0]["message"]["content"])

💬 4. Write Prompts Like Product Specs

Treat prompts as structured specifications:

• Define the agent’s role, goal, and constraints.
• Use step-wise reasoning (“think step-by-step”) for clarity.
• Include examples and explicit output formats.
• Add instructions like “return JSON” or “limit to 3 bullet points”.

🧪 5. Evaluate in Realistic Scenarios

Test agents end-to-end, not just via unit tests. Build datasets with:

• Success and failure cases
• Tool errors and edge conditions
• Human feedback integration

Use automated evaluation pipelines and track metrics such as accuracy, latency, and pass rates.

🔐 6. Secure Your Agent: Prevent Prompt Injection

Prompt injection is when user input manipulates your system instructions.
To mitigate:

1. Isolate inputs – never concatenate user text into prompts.
2. Limit privileges – use allowlists for commands and API scopes.
3. Sandbox execution – no arbitrary file or shell access.
4. Validate context – remove untrusted inputs after use.
5. Red-team tests – simulate attacks regularly.

Safe Plan-Then-Execute Pattern

def create_plan(goal: str):
    if goal == "book_flight":
        return ["search_flights", "select_flight", "book_ticket", "send_receipt"]
    raise ValueError("Unsupported goal")

def execute_plan(plan, context):
    for step in plan:
        if step == "search_flights":
            flights = search(context["from"], context["to"], context["date"])
        elif step == "select_flight":
            choice = pick_best(flights)
        elif step == "book_ticket":
            confirm(choice)
        elif step == "send_receipt":
            email_receipt()

🤝 7. Multi-Agent Architecture & Least-Privilege Permissions

Break down large agents into smaller, specialized subagents coordinated by an orchestrator.

Each subagent should:

• Have minimal permissions and isolated context.
• Log tool calls for observability.
• Reset memory after session boundaries.

Use telemetry (e.g., OpenTelemetry) for tracing and monitoring across agents.

🔁 8. Continuous Improvement

Reliability is iterative.
Use:

• Feedback loops (human-in-the-loop review)
• Regression evaluation
• Prompt versioning
• Context pruning
• Feature flags for rollout

🪞 9. Self-Evaluation and Reflection Loops

Modern agents can critique their own reasoning.
This improves accuracy, factuality, and consistency.

Reflection Loop Example

def generate(model, prompt):
    return model.chat([{"role": "user", "content": prompt}]).content

def reflect(model, draft):
    critique_prompt = (
        "You are a reviewer. Identify missing details, incorrect facts, "
        "and suggest improvements for this draft:\n\n" + draft
    )
    return model.chat([{"role": "user", "content": critique_prompt}]).content

def reflection_pipeline(model, question, iters=2):
    answer = generate(model, question)
    for _ in range(iters):
        feedback = reflect(model, answer)
        revised_prompt = f"Revise your answer using this feedback:\n{feedback}"
        answer = generate(model, revised_prompt)
    return answer

Reflection Flowchart

Benefits:

• Catch reasoning errors before output.
• Encourage step-wise improvement.
• Reduce hallucination rates.
• Improve user satisfaction.

✅ Final Takeaways

Reliable LLM agents require:

• Structured design and memory control
• Secure tooling and sandboxing
• Continuous evaluation and reflection
• Strong governance, logging, and observability

By following these principles, you can build LLM agents that are not just powerful—but predictable, auditable, and safe for production.