Over the past 6 months, I've been running AI agents I designed for different workloads autonomously. This experience largely shaped my expectations and gave me a very clear picture of the current capabilities and future potential of AI agents. This journey, which I started with great excitement, both surprised me and confronted me with some realities.
In this post, based on my own experience, I will explain everything from the setup and optimization of AI agents to the technical challenges I faced and my cost analysis, as transparently as possible. My goal is to provide an honest, field-based report to anyone interested in this area or considering developing their own agents.
What Was My Initial Approach to the AI Agent Concept?
AI agents can fundamentally be defined as software entities designed to achieve a specific goal, with the ability to make autonomous decisions and take action. My initial interest was in how efficient these agents could be in repetitive, rule-based, yet flexible business processes. I've always wondered about areas that could be automated without human intervention, especially in a manufacturing ERP or in the back-end processes of my own side product.
The general consensus was that AI agents were not just simple scripts, but rather capable of performing complex tasks with dynamic planning, tool usage, and feedback loops. While this sounded great in theory, I wanted to see their real-world applications and limitations. Therefore, instead of just generating a one-time answer based on a prompt, I aimed to build structures that could follow a series of steps on their own and correct themselves when necessary.
Which Workloads Did I Decide to Try AI Agents For?
During my trial, I identified several different workloads. Foremost among these was generating content ideas and drafting texts for my bilingual technical blog. This was a relatively low-risk but high-repetition potential area. Secondly, I tried to automate the task of analyzing supply chain data in a manufacturing ERP to detect potential disruptions early and report them. This was a more critical and complex task.
A third area of experimentation was analyzing new spam patterns and generating rule suggestions for my Android spam blocker application. This task required working with constantly changing datasets and expected rapid adaptation capabilities. Finally, there were a series of system monitoring and log analysis tasks running on my VPS; they were expected to detect anomalies and summarize them for me. Each task was chosen to test different capabilities of the agent.
💡 Choosing Trial Areas
When selecting your initial trial areas for AI agents, prefer tasks that are both low-risk and have high potential returns. This minimizes the risk of damaging large systems until you understand the agent's capacity. Your own blog content or simple data analysis are ideal for such beginnings.
How Was the Agent Architecture Set Up and What Did I Use?
I tried to keep my agent architecture as flexible as possible. I fundamentally built a structure consisting of a Controller agent, a Planner agent that breaks down tasks, and Executor agents that perform each sub-task. I ensured communication between them via a message queue like RabbitMQ. For LLMs, I initially used Gemini Flash and Groq APIs, then later incorporated different providers like Cerebras via OpenRouter to create fallback mechanisms. Groq, in particular, was perfect for Executor agents due to its low latency.
On the Prompt Engineering side, I used a separate "system prompt" and tool definitions for each agent. I applied the RAG (Retrieval-Augmented Generation) pattern, especially for ERP data and the historical content of my technical blog. I performed vector searches using the pg_vector extension in PostgreSQL. You can see a simplified flow diagram below:
This structure offered the flexibility to switch to another agent if one failed. For example, when I hit Groq's token limit, I automatically redirected to Gemini Flash. This multi-provider fallback strategy played a critical role in the uninterrupted operation of the agents, especially in the first few months. The Python code structure for this system I built for my side product was as follows:
# executor_agent.py
import os
from openai import OpenAI # OpenRouter compatible
from google.generativeai import GenerativeModel # Gemini
class ExecutorAgent:
def __init__(self, llm_provider="groq"):
self.llm_provider = llm_provider
self.client = self._init_client()
def _init_client(self):
if self.llm_provider == "groq":
return OpenAI(api_key=os.getenv("GROQ_API_KEY"), base_url="https://api.groq.com/openai/v1")
elif self.llm_provider == "gemini":
return GenerativeModel(os.getenv("GEMINI_API_KEY"))
elif self.llm_provider == "openrouter":
return OpenAI(api_key=os.getenv("OPENROUTER_API_KEY"), base_url="https://openrouter.ai/api/v1")
else:
raise ValueError("Unsupported LLM provider")
def execute_task(self, prompt, tools=None):
messages = [{"role": "user", "content": prompt}]
try:
if self.llm_provider == "gemini":
# Special handling for Gemini API as it works differently
response = self.client.generate_content(prompt)
return response.text
else:
response = self.client.chat.completions.create(
model="llama3-8b-8192" if self.llm_provider == "groq" else "mistralai/mistral-7b-instruct:free",
messages=messages,
tools=[t.schema for t in tools] if tools else None,
tool_choice="auto" if tools else "none"
)
if response.choices[0].message.tool_calls:
# Tool call logic
return {"tool_calls": response.choices[0].message.tool_calls}
return response.choices[0].message.content
except Exception as e:
print(f"Error executing task with {self.llm_provider}: {e}")
raise
# The Controller agent used this ExecutorAgent to switch between different providers.
First 3 Months: What Were the Successes and Disappointments?
In the first three months, I achieved noticeable successes, especially in more flexible tasks like blog content generation and spam pattern analysis. Weekly production of 3-4 draft texts for the blog was acceptable quality at a 70% rate, meaning they could be published with only minor corrections. This significantly accelerated my content production process and reduced the burden on human writers. Similarly, 85% of the suggested rules for the Android spam blocker contained accurate detections, reducing manual analysis time from an average of 2 hours to 30 minutes.
However, I couldn't achieve the expected performance in the supply chain analysis task within the manufacturing ERP. The agent struggled to understand complex business rules and exceptions. For example, it consistently misinterpreted a multi-layered rule like "if the stock of raw material X falls below a critical level and supplier Y's delivery time is more than Z days, research alternative suppliers." While it initially reported with 60% accuracy, this rate dropped to 35% with manual verification. Especially in situations involving "fuzzy logic," the non-deterministic nature of the agent seriously affected reliability.
⚠️ Caution with Complex Business Rules!
While AI agents succeed in tasks with clear and specific rules, they can experience serious reliability issues when complex business rules that are open to interpretation, full of exceptions, or require "common sense" are involved. Human-in-the-loop oversight is indispensable in such tasks.
Furthermore, the agents' tendency to "hallucinate" became a problem, especially in critical data analysis tasks. On one occasion, it generated a "risk report" about a completely fabricated supplier name and delivery date that did not come from the ERP system. This once again showed how important my manual control mechanisms were. To obtain reliable output, it was necessary to verify the data used by the agent at each step and the conclusions it reached. This dealt a serious blow to my autonomy goals.
Next 3 Months: What Were the Optimizations and Deep Lessons Learned?
After my experiences in the first three months, I made significant optimizations, especially for my ERP and system monitoring agents. The biggest lesson was to provide more "grounding" to the agents. By strengthening the RAG system, I ensured that the agent used not only general LLM knowledge but also current and verified internal company documents, workflow diagrams, and rule sets. By optimizing pg_vector indexes in PostgreSQL (e.g., using HNSW instead of IVFFlat), I increased vector search performance by 30% and enabled the agent to retrieve more relevant context.
On the Prompt Engineering side, I added more specific instructions and "thought process" guidelines for each step. I asked the agent not only for the answer but also to explain how it arrived at the answer, which tools it used, and what information it referenced. This greatly facilitated the debugging process. For example, the steps the ERP agent had to follow before performing a task were defined as follows:
# ERP Supply Chain Analysis Agent - Task Steps
1. Analyze the incoming request and extract necessary parameters (product code, date range, critical stock level).
2. Retrieve stock, order, and shipment data for relevant products from the database (PostgreSQL). Clearly state the SQL query.
3. Identify products that fall below the critical stock level in the retrieved data.
4. Query the historical delivery times and average delay rates of current suppliers for these products.
5. If the risk of delay is high for a supplier (e.g., more than 5 days delay in 2 out of the last 3 deliveries), find a list of alternative suppliers and their average delivery times.
6. Summarize all this information and present it in a "Risk Report" format. The report should include the risky product, current supplier, reason for risk, suggested alternatives, and estimated impact (e.g., production loss).
7. After generating the report, explain all steps and their reasons step-by-step.
Thanks to these detailed prompts, the ERP agent's accuracy rate increased from 35% to 70%. It still wasn't perfect, but at least the frequency of generating fabricated information significantly decreased. Additionally, I integrated "Human-in-the-loop" mechanisms more thoroughly. Before critical decisions or for reports falling below a certain confidence threshold, I mandated manual approval by sending a Slack notification. This enabled agents to be used confidently, especially in sensitive areas like financial calculators and customer projects.
What Was the True Cost and My ROI Calculation for AI Agents?
The cost of running AI agents for 6 months was more complex than I thought and not just about API fees. The biggest items were:
- LLM API Fees: On average, this amounted to around 150-250 USD per month. Groq was advantageous here due to its low cost per token despite its high performance. However, when I used models like Gemini or GPT-4 for more complex tasks, this cost could quickly increase. Especially embeddings and vector searches for RAG also constituted a small but continuous cost.
- Infrastructure Costs: Agents running on my own VPS (DigitalOcean), RabbitMQ, PostgreSQL, and the RAG vector database incurred an average additional cost of 80 USD per month. Particularly, compute-intensive RAG queries and parallel agent executions increased CPU and RAM usage.
- Development and Maintenance Time: This was the most important but overlooked cost item. I spent approximately 1.5 months full-time on initial setup and prompt engineering. For the subsequent 5 months, I dedicated an average of 10-15 hours per week to optimizing agents, fixing errors, and integrating new tools. This equates to a significant portion of a developer's monthly salary. For example, when I encountered an N+1 query problem in a manufacturing ERP, first examining the agent's logs and then reviewing the query plan with
EXPLAIN ANALYZEon the database side took a considerable amount of my time to understand the agent's error.
Regarding my ROI (Return on Investment) calculation: for relatively simple tasks like blog content generation and spam pattern analysis, agents indeed saved human labor. Producing 3-4 draft texts per week for the blog covered about 5-6 hours of a content writer's weekly work. This meant a saving of approximately 20-24 hours per month. In spam analysis, it reduced 50 hours of monthly manual analysis to 10 hours. The total savings in these two areas more than covered the LLM and infrastructure costs.
However, for complex tasks like supply chain analysis in ERP, the ROI was negative. Due to the low reliability of the reports generated by the agent, each report had to be manually checked and corrected. This nullified the agent's promise of "automation" and sometimes even caused me to spend more time compared to the manual process. Here, the cost of agent error (a wrong decision made due to an incorrect report) also had to be considered.
How Will My Approach to AI Agents Be in the Future?
After this intense 6-month experience, my perspective on AI agents has become much more realistic. I now see agents less as "task hunters" and more as "smart assistants" or "data collectors." Full autonomy, especially in complex and high-risk tasks, is still beyond imagination. The greatest strength of agents emerges when they work in conjunction with human intervention.
In the future, I aim to use agents more in the following areas:
- Data Preprocessing and Summarization: Quickly scanning large datasets (e.g., logs, financial records) and extracting meaningful summaries. Specifically, detecting anomalies in
journaldlogs and presenting me with a consolidated report could greatly simplify system administration. - Information Retrieval and Synthesis: Further developing the RAG architecture to retrieve relevant information from internal knowledge bases (Wiki, documents, past emails) and synthesize it to answer a specific question. This could accelerate the onboarding process for new employees or shorten problem-solving time for technical support teams.
- Automated Test Scenario Generation: In software development processes, generating test scenarios and even simple unit test code for a specific feature. This could increase the reliability of my CI/CD pipeline. I would like to see an agent automatically generate test cases when a new API endpoint is added in a customer project.
- Simple Form Filling or Data Entry: Especially repetitive, rule-based data entry tasks performed via web interfaces.
ℹ️ Agents in a Zero-Trust Architecture
Applying Zero-Trust principles when designing AI agents' access permissions and security policies is critical. Each agent having the least privilege necessary only for the job it needs to do minimizes potential security vulnerabilities. For example, an agent should only have
SELECTpermission on a specific database table, notUPDATEorDELETE.
