DEV Community: yeucongnghevm

From AI Prototype to Production: 7 Problems That Break AI Agents

yeucongnghevm — Mon, 15 Jun 2026 15:14:05 +0000

Building an AI agent prototype is relatively easy. With an LLM, a retrieval pipeline, and several API connections, developers can create an impressive demonstration within days.

The real challenge begins when the system reaches production.

Real users submit unclear requests, external tools fail, business data changes, and model costs increase unexpectedly. An agent that performs well in a controlled test may become unreliable when thousands of people start using it.

A Real-World Example: Vanta’s Support Agent

Vanta provides a useful example of how an AI agent should be tested before full deployment.

According to an Intercom customer story, Vanta evaluated Fin AI Agent against its existing AI system using 400 real customer conversations. Fin resolved approximately 73% of the cases, compared with around 49% for the existing system.

After deployment, the agent achieved a 71% resolution rate for the chat conversations it handled. This represented nearly 2,500 conversations per month that did not require a human support agent.

The results are impressive, but the evaluation process is equally important. Vanta did not rely on a polished demo. It tested the agent with real questions and measured resolution rate, accuracy, and answer quality before expanding its use.

Here are seven problems developers should address when moving an AI agent into production.

1. Hallucinated Answers

LLMs can generate confident responses without reliable evidence. RAG can reduce this risk by connecting the agent to trusted information, but retrieved content must still be relevant and current.

2. Poor Retrieval Quality

A retrieval system may return incomplete, outdated, or unrelated documents. Evaluate retrieval separately using metrics such as precision, recall, relevance, and answer faithfulness.

3. Failed Tool Calls

Agents often depend on APIs, databases, search services, or MCP servers. These tools may time out or return invalid data.

def call_tool_safely(tool, arguments):
    try:
        result = tool(**arguments)
        return result if result else {"error": "Empty response"}
    except TimeoutError:
        return {"error": "Tool timed out"}

Production workflows need retries, timeout limits, validation, and fallback responses.

4. Uncontrolled Agent Loops

An agent may repeatedly plan and call tools without completing the task. Set limits for tool calls, reasoning steps, execution time, and cost per request.

5. Excessive Permissions

Agents should not have unrestricted access to business systems. Use role-based permissions and require human approval for sensitive actions such as issuing refunds or deleting data.

6. High Latency and Cost

Multiple model calls and retrieval steps can make an agent slow and expensive. Use caching, shorter prompts, parallel execution, and smaller models for simple tasks.

7. Missing Observability

Without tracing, developers cannot determine whether an error came from retrieval, the model, or an external tool.

A useful trace should capture prompts, retrieved documents, tool calls, errors, latency, token usage, cost, and final responses.

Production Readiness Is a System Problem

A reliable AI agent is more than an LLM connected to several tools. It requires testing, security, observability, fallback logic, and continuous evaluation.

Organizations building complex AI products may also work with an experienced technology partner. Varmeta develops AI and data solutions that help businesses transform early concepts into scalable production systems.

The best AI agents are not those that perform perfectly in a demo. They are those that remain useful when tools fail, data changes, and real users behave unpredictably.

Source: Intercom, “How Vanta unified its customer experience with Fin.”

Stop Talking, Start Doing: A Beginner’s Guide to Agentic AI

yeucongnghevm — Sun, 26 Apr 2026 11:14:27 +0000

If you have used ChatGPT or Claude, you have experienced the "Generative" side of Artificial Intelligence. It’s impressive at writing code, drafting emails, and answering questions. But in the professional world, "talking" is only half the battle. The real value lies in "doing." This is the fundamental shift from traditional AI to Agentic AI.

For a beginner, the easiest way to understand this is: If a standard AI is like an encyclopedia that can talk back to you, an Agentic AI is like a highly skilled intern who can actually go out and complete tasks for you.

The Pain Point: The "Manual Work" Trap

Imagine you are a software developer managing a small cloud infrastructure. You receive an alert that your server is running out of storage.

With Standard AI: You copy the error log, paste it into a chatbot, and ask for a command to clear the cache. The AI gives you the command, and you have to log in and run it.
The Problem: You are still the bridge between the AI's knowledge and the actual system. This manual intervention is a bottleneck that prevents true scaling.

According to research on Agentic Workflows by Andrew Ng's DeepLearning.AI, the ability for AI to iterate and use tools is often more important than the size of the model itself.

Agentic AI removes the human-in-the-middle. Instead of just suggesting a command, an Agentic system is designed to:

Perceive: Identify the low-storage alert automatically.
Plan: Decide that clearing temporary logs is the safest first step.
Act: Securely log into the server and execute the cleanup.
Report: Send you a notification saying, "I noticed the storage issue and fixed it. Here is what I did."

This isn't just automation; it’s autonomous reasoning. It uses a "Plan-Act-Observe" loop to ensure the goal is met.

How Does It Actually Work Under the Hood?

To achieve this level of autonomy, Agentic AI relies on a combination of core technologies. It doesn't just use a Large Language Model (LLM) to generate text. It connects the LLM to external tools via APIs, allowing it to interact with web browsers, code interpreters, and internal databases.

Furthermore, these agents use memory systems (like Vector Databases) to remember past interactions and learn from previous mistakes. When a task fails, the agent doesn't just crash; it analyzes the error output, adjusts its strategy, and tries a new approach.

Why Start Your Agentic Journey Now?

The transition from simple prompts to autonomous agents is the biggest trend in tech today. For businesses and developers, the goal is no longer just to generate text, but to build systems that can operate independently.

If you're looking for a deep dive into how these systems are structured, exploring a comprehensive Agentic AI guide is the perfect starting point. These frameworks allow you to move beyond simple chat interfaces and start building AI that actually works for you, freeing up your time for higher-level creative strategy.

Final Thoughts: Agentic AI isn't about replacing humans; it's about elevating them. By offloading repetitive execution to autonomous agents, we can focus on what we do best: innovating and solving the world’s most complex problems.

Prompt Engineering is Dead: Why Agentic AI is the New Frontier for Developers

yeucongnghevm — Sat, 11 Apr 2026 11:00:13 +0000

Let’s be honest: we’re all a bit tired of "Prompt Engineering." Spending hours tweaking a 500-word instruction just to get an LLM to follow a specific format is not sustainable.

As developers, we are moving away from being "Prompt Whisperers" to becoming Agentic Architects. The era of Agentic AI has arrived, and it changes everything we know about building intelligent systems.

The Shift: From Passive Models to Active Agents

A standard LLM is a "brain in a box." It’s brilliant, but it’s passive. It waits for you to trigger it.

Agentic AI, however, is a brain with hands. It doesn't just predict the next token, it pursues a goal. Instead of asking AI to "Write a summary," we are now building systems that can:

Self-Plan: Break a goal into 10 sub-tasks.
Use Tools: Call an API, query a SQL database, or run a Python script.
Self-Correct: If Step 3 fails, the agent analyzes the error and tries a different path.

Why Devs Love Agentic Architectures

For those of us in the trenches of software development, Agentic AI solves the "last mile" problem of automation.

Workflow Integration: You no longer need to hard-code every edge case. The agent handles the logic of "what to do if X happens."
Reduced Context Bloat: Instead of sending massive prompts, you give the agent access to specific tools (tools-use) as needed.
True Scalability: One developer can manage an entire fleet of autonomous agents performing research, testing, or data migration.

The New Tech Stack

Building with Agentic AI requires a shift in mindset. We are moving from simple Chat interfaces to Autonomous Reasoning Engines. This involves managing:

Memory Layers: How agents remember past failures across different sessions.
Tool-sets: Defining the "sandbox" where your agent can safely execute code.
Orchestration: Managing how multiple agents talk to each other to solve a single problem.

If you’re still focusing on how to write the "perfect prompt," you’re missing the forest for the trees. The real value is in the system design, the orchestration of autonomous agents.

Conclusion

The transition from Generative AI to Agentic AI is the most significant leap in software architecture this decade. It’s time to stop talking to the AI and start building systems that let the AI work for you.

To dive deeper into the technical frameworks behind these systems, check out this comprehensive guide on Agentic AI.

The Evolution of Automation: Why Agentic AI is the Next Frontier in 2026

yeucongnghevm — Sat, 11 Apr 2026 10:29:04 +0000

The digital landscape is shifting rapidly. For the past few years, the world has been captivated by Generative AI, its ability to write essays, generate images, and summarise complex documents. However, we are now entering a new era where AI doesn't just "talk" or "create content", it acts.

This shift is defined by the rise of Agentic AI, a transformative technology that moves beyond simple prompts to autonomous execution.

What Makes Agentic AI Different from Generative AI?

At its core, Agentic AI refers to artificial intelligence systems designed to function as "agents" rather than mere assistants.

While a standard LLM (Large Language Model) waits for a human to provide a specific instruction for every step, an agentic system is goal-oriented.

When given a high-level objective, these systems can reason, plan, and use external tools to complete multi-step workflows. They can browse the web, interact with software APIs, manage databases, and even correct their own errors without constant human intervention.

This transition from "passive response" to "active pursuit" is what sets this technology apart in the current tech ecosystem.

The Business Impact of Autonomous Agents

For businesses looking to scale, the implications of Agentic AI are profound.

Traditional automation often relies on rigid, "if-this-then-that" logic. Agentic systems, however, bring a level of cognitive flexibility that allows them to handle uncertainty.

1. Complex Problem Solving

Instead of just generating a report, an AI agent can identify a data discrepancy, investigate its source, and propose a solution automatically. It doesn't just flag a problem; it works toward solving it.

2. Operational Efficiency

By handling repetitive, multi-layered tasks—such as end-to-end customer onboarding or supply chain coordination—human teams are freed to focus on high-level strategy and creativity.

3. Continuous Improvement

These agents can learn from their environment, optimizing their workflows over time to achieve better results. They aren't static tools; they are evolving digital employees.

Why Integration is the Key to Success

The jump from Generative AI to truly autonomous systems requires a robust framework. It isn't just about having a powerful model, it’s about how that model interacts with your existing digital ecosystem.

Companies are now looking for ways to implement these "reasoning engines" into their core operations to maintain a competitive edge.

The future belongs to those who can leverage these autonomous capabilities to drive innovation. As we move forward, the question won't be whether you are using AI, but rather how much autonomy you are giving your AI to deliver real-world results.

Conclusion

We are moving away from a world of static tools and toward a world of dynamic collaborators. By understanding and implementing the principles of Agentic AI, organisations can unlock levels of productivity that were previously thought impossible.

The journey toward full autonomy is just beginning, and staying informed is the first step toward leadership in the AI-driven economy.