Why GenAI Apps Could Fail Without Agentic Frameworks

Did you ever feel like working with AI frameworks is like having a conversation with a boring husband (as per 99.99% wives) who only answers when asked but never takes the initiative? 

In one of my previous blogs, we discussed LangChain, which opened up the possibility of chaining together different AI models and tools. But here’s the catch: LangChain and similar frameworks are fundamentally reactive. They answer questions, process inputs, and carry out predefined steps, but they don’t exactly “think” for themselves or solve complex workflows automatically. This is where the need for agentic frameworks arises.

So, what are these “Agentic” frameworks? And why do we need them?

Think of them as the difference between giving a person a list of tasks versus giving them a goal and letting them figure out the best way to achieve it. Agentic frameworks make AI applications not just responsive but proactive, goal-oriented, and capable of handling dynamic, real-world challenges.

What Is an Agentic Framework?

A framework designed to build agents — pieces of software that act autonomously to achieve specific goals. Unlike typical pipelines or workflows that require explicit instructions at every step, agents leverage context, reasoning, and goal-setting to determine the best course of action on their own. In short, they’re built to “figure it out.”

Let’s take a real-world example.

Imagine building a virtual personal assistant app. Without an agentic approach, you’d have to specify each step — “check emails,” find today’s tasks,” “sort by priority,” etc. But with such a framework, you could set a goal, like “help me plan my day,” and the agent would determine what tasks to prioritize based on context — like you calendar, emails, and meeting notes.

Why Do We Need Agentic Frameworks?

Let’s look at a popular GenAI application like ChatGPT. Right now, when you ask ChatGPT for help, it responds to each prompt individually, with no memory of prior conversations (beyond the current session) and no ability to self-initiate actions or goals. For example, if you ask the app to “help me plan my day,” it will suggest a schedule, but it won’t proactively update it or remind you as the day progresses. Each time you want an update, you’d need to come back and provide more specific prompts.

Without agentic frameworks, GenAI tools remain fundamentally reactive. They’re great at answering questions, generating responses, and even assisting with structured tasks, but they rely on user-driver commands. Every step requires user input, and they can’t autonomously manage goals or make ongoing adjustments to fit changing needs.

Here’s Why They Are Essential:

1. Handling uncertainty: In real-world applications, data is rarely as predictable or structured as we’d like. An agentic framework can adapt to changing conditions, like fluctuating data or unforeseen variables, without needing constant reprogramming.
2. Optimizing efficiency: They allow AI to make decisions about resource allocation, speeding up processes. For example, an agentic AI could “decide” to prioritize urgent tasks during peak hours or process less critical tasks when server loads are low.
3. Reducing human oversight: With traditional frameworks (yes, that’s so last week/month/year), developers or operators (like you and me) constantly tweak workflows to align with updated goals or data. An agentic framework reduced this need, allowing AI to adjust its behavior autonomously based on higher-level objectives.

How Agentic Frameworks Work: The Core Components

Most agentic frameworks operate using a combination of these key components:

1. Goal definition: You specify a high-level objective rather than a rigid series of steps.
2. Context awareness: The framework maintains and uses context about its environment or previous interactions.
3. Reasoning and decision-making: The agent evaluates different actions to determine the best path forward.
4. Feedback loops: Feedback loops let agents learn from past actions to improve their responses.

Let’s try to visualize the difference in processing.

Enough stories, I guess; let’s try creating a basic agent with Python.

import random
class StockAgent:
    def __init__(self, stocks):
        self.stocks = stocks # Dict of stock symbols and values

def evaluate_market(self):
        # Randomnly decide if the market is up or down
        return "up" if random.choice([True, False]) else "down"
    def adjust_portfolio(self):
        market_condition = self.evaluate_market()
        if market_condition == "up":
            print("Market is up! Holding stocks.")
        else:
            print("Market is down! Considering adjustments...")
            # Sell high-risk stocks or adjust allocation based on context
            for stock, value in self.stocks.items():
                if value < 50:
                    print(f"Selling {stock} to mitigate risk.")
                else:
                    print(f"Keeping {stock}, as it's performing well.")
    
    def monitor(self):
        print("Monitoring portfolio...")
        self.adjust_portfolio()
# Initialize agent with sample stock data
stocks = ("AAPL": 120, "TSLA": 45, "AMZN": 200)
agent = StockAgent(stocks)
# Run the agent
agent.monitor()

This is a simple agent that checks the market condition and makes autonomous decisions based on a few pre-defined rules. In a more advanced framework, this agent would access real-time data, assess long-term goals (like maximizing profit), and adapt its strategy over time.

Existing Agentic Frameworks: Langchain and Beyond

Langchain has its own set of agents, but other frameworks are emerging that specialize in agentic capabilities. For example, Auto-GPT is an experimental project that chains tasks autonomously, allowing the agent to pursue a high-level objective without explicit step-by-step guidance. 

Similarly, GPT-Engineer uses an agentic approach to help generate code iteratively, simulating an autonomous coding partner.

Key Challenges in Building Agentic Frameworks

Agentic frameworks aren’t a cure-all, and they come with their own challenges:

1. Complexity in debugging: Since agents act on their own, understanding why they made a certain decision can be challenging.
2. Resource intensity: Agents require ongoing processing power to evaluate conditions and make decisions dynamically.
3. Ensuring security and ethics: Autonomous agents operating with little oversight can inadvertently take actions that violate privacy or ethical guidelines.

Conclusion: Embracing the Agentic Mindset

Agentic frameworks offer a powerful way to build AI applications that go beyond simply reacting. They allow us to create systems that understand goals, act with autonomy, and adapt to new data — all with minimal intervention.

It isn’t a magic bullet but a critical step towards making AI applications more intelligent, adaptive, and useful in complex, real-world environments. As we move forward, expect agentic frameworks to become foundational tools for developers looking to build applications that can truly handle dynamic, ever-changing tasks.

What are your thoughts and experiences around agentic frameworks? Let me know in the comments sections below and let's discuss the endless possibilities.