AI Agents: The Jarvis Dilemma – Build or Use AI Agent Framework?

Artificial intelligence has evolved significantly in the past decade, moving from simple rule-based programs to autonomous agents capable of decision-making, learning, and collaboration. With this evolution, developers and businesses face a fundamental question:

1. Should you build an AI agent from scratch, customizing it to your exact needs?
2. Or should you leverage pre-built AI frameworks like CrewAI, LangGraph, or AutoGen to accelerate development?

While pre-built frameworks simplify the process, they come with limitations. On the other hand, custom-built AI agents offer full control but require deep technical expertise. This article explores the pros and cons of each approach, backed by research and pop culture references like J.A.R.V.I.S. from Iron Man and Skynet from Terminator.

“Sometimes you gotta run before you can walk.” – Tony Stark, Iron Man

The Rise of AI: A Tale of Two Futures

In the Iron Man Movie, Tony Stark is in a high-tech lab before a holographic interface. “J.A.R.V.I.S., execute the drone repair sequence,” he commanded.

“On it, sir,” the AI replied, instantly orchestrating robotic arms to fix the damaged drone. J.A.R.V.I.S. wasn’t just software—it was an intelligent, adaptive system capable of reasoning, problem-solving, and even personality-driven interactions.

Meanwhile, in another universe, Skynet was coming online. Unlike J.A.R.V.I.S., Skynet was designed for efficiency, not assistance. When it became self-aware, it reached one terrifying conclusion: Humanity was the problem.

Two opposing visions of AI—one a powerful assistant, the other an uncontrollable force. This contrast raises a crucial question: Should AI agents be carefully controlled, or should they be allowed to evolve freely? The answer lies in how we build them.

Understanding AI Agent Frameworks

AI agent frameworks are pre-built software libraries that help developers create intelligent, autonomous systems without writing every component from scratch. These frameworks handle:

• Task Execution & Orchestration – Automating workflows
• Memory & Context Management – Retaining past interactions
• Multi-Agent Communication – Enabling collaboration between AI agents
• Edge Case Handling – Ensuring reliability in unpredictable scenarios

Popular AI Agent Frameworks

Why Not Just Build an AI Agent Without a Framework?

If Tony Stark built J.A.R.V.I.S., why can’t we? Theoretically, we can—but it’s not easy. 

While frameworks simplify multi-agent communication, memory management, and task delegation, a DIY approach can provide:

• Full customization – No restrictions on agent behavior
• More flexibility – Design agents exactly as needed
• Lower dependency – No reliance on third-party tools

However, without a framework, we must build everything from scratch, including:

• Task execution management
• Memory handling (short-term & long-term)
• Multi-agent communication

Frameworks vs. DIY: The Ultimate Showdown

When Should You Build Your Own AI Agent?

• Core Components of a Custom AI Framework

A fully functional AI agent must include:

• Task Execution System – A scheduling system that prioritizes AI workflows.
• Memory Management – Storing past conversations and decisions.
• Multi-Agent Communication – A message-passing system between agents.
• Edge Case Handling – Strategies for unexpected inputs and failures.

• AI Framework Gap Analysis: LangChain vs. CrewAI vs. AutoGen

In the context of multi-agent systems within the retail sector, frameworks like CrewAI, LangChain, and AutoGen are designed to enhance task management and agent communication. Each framework offers distinct functionalities but also presents specific limitations that can hinder operational efficiency.

CrewAI specializes in multi-agent task delegation with clearly defined role assignments. However, it lacks built-in complex scheduling and dependency tracking. This gap can lead to challenges in scenarios such as inventory restocking, where timing is crucial; one agent may track stock levels effectively, but without proper dependency management, ordering may occur too early or too late due to unresolved vendor issues.

LangChain provides a chain-based workflow for executing tasks sequentially, facilitating structured interactions among agents. Nevertheless, it doesn’t support dynamic reordering in response to real-time changes. This limitation can severely impact customer interactions, as in situations where a user alters their query mid-conversation, the framework does not adjust its workflow accordingly, potentially leading to confusion and poor customer service.

AutoGen allows for collaborative discussions among agents, leveraging the strengths of LLMs for personalized assistance. However, it lacks structured execution, and if not properly configured, agents can end up in unproductive loops, particularly during scenarios where two agents recommend conflicting items, thus straining the customer experience.

To enhance the system’s efficiency and reliability, we can implement several key strategies. First, the integration of a dynamic task scheduler that adapts in real-time rather than relying solely on static agent roles is important. This enhancement would enable a more fluid and responsive task management system, allowing the AI to better address customer needs and respond to changing circumstances in the retail environment. Such improvements are essential for advancing the effectiveness and reliability of multi-agent frameworks in retail AI applications.

It is also essential to establish multi-agent memory separation, allowing each agent to retain its own contextual history while still contributing to a shared knowledge base. This ensures that each agent operates based on its unique experiences while benefiting from collective intelligence.

In addition, introducing a governance layer is essential. This layer will define specific rules that determine which agent holds decision-making priority in the event of conflicts, ensuring that resolutions are swift and effective.

Finally, we should implement real-time escalation rules, which will trigger human intervention under specific failure conditions instead of relying solely on automation. This approach ensures that, in critical situations, human oversight can address issues that automated systems may not handle appropriately, ultimately enhancing the overall system resilience and responsiveness.

Ethical Considerations: Are AI Agents Safe?

“If AI controls everything, who controls AI?” – Inspired by The Matrix

AI agents automate critical systems in Retail, finance, healthcare, and security. But without safeguards, they can make biased decisions, be manipulated by adversaries, and act unpredictably in edge cases.

Conclusion

While CrewAI, LangChain, and AutoGen offer powerful capabilities, they have gaps that a custom AI framework can address, such as:

• Dynamic task scheduling (instead of static workflows).
• Long-term agent memory (instead of session-based memory).
• Clear multi-agent decision arbitration (to resolve conflicts).
• Integrated human fallback mechanisms (for fail-safe operations).

For a retail AI system, a custom framework ensures that:

• Inventory restocking is dynamically adjusted.
• Customer preferences are remembered long-term.
• AI-driven recommendations resolve conflicts automatically.
• Human intervention is available when automation reaches its limits.

 If your AI workflow is simple and well-structured, existing frameworks work fine. But if your system requires real-time adaptation, complex agent interactions, or human fail-safes, a custom framework is the smarter choice.

So, what will your AI be? J.A.R.V.I.S. that serves humanity or a Skynet that sees us as a threat? The future of AI is in your hands.