As artificial intelligence (AI) continues to evolve, the idea of fully autonomous agents that can independently make decisions and act based on user input is becoming more than just a fantasy—it’s edging closer to reality. However, the journey to productionizing these agents is fraught with unexpected hurdles, as experienced by teams working at the frontier of agent-based applications.
In this blog post, we’ll explore the key learnings from developing agentic applications, diving into the operational complexities and challenges of backward compatibility, feature control, and ethical alignment. These insights offer a behind-the-scenes view into the difficulties of building agentic systems and highlight the critical considerations developers must consider.
What Are Agentic Applications?
Before diving into the challenges, it’s important to understand what we mean by “agentic applications.” In the simplest terms, these are applications built around AI agents—models capable of performing tasks, making decisions, and interacting with users autonomously. These agents are typically powered by large language models (LLMs) like GPT-4 or Meta’s LLaMA series, which can understand natural language input and generate responses or actions based on that input.
However, while these agents sound powerful, turning them into reliable, production-ready applications is far more complex than traditional software development.
1. The Paradigm Shift: From Fixed Flows to Agentic Applications
Traditional software engineering operates on predictable, deterministic flows. Developers write specific instructions for the system to follow, and testing processes ensure the software behaves in predefined ways. Agentic applications, on the other hand, introduce a new paradigm: freedom of action. Rather than following a set path, the AI agent can explore different ways to accomplish its tasks. It must be flexible enough to adapt to various inputs and situations.
One of the key challenges lies in operational complexity. Moving from fixed workflows to agentic applications isn’t just an incremental change—it’s a step change in difficulty. The unpredictability inherent in agentic applications creates new challenges when bringing these agents into production. Developers can no longer take anything for granted. Every potential path the agent might take must be considered, evaluated, and tested, drastically increasing the burden on teams to ensure these systems are safe and reliable.
2. The Backward Compatibility Problem
In the world of traditional software, backward compatibility is a given. When updating software, developers ensure that existing functionality works as expected. However, this concept is often absent in AI-driven systems.
The challenge here is that agentic applications are built around models—typically LLMs like GPT-4 or LLaMA. If a team creates an application using one version of a model (e.g., LLaMA 3), they may expect that transitioning to a newer version (e.g., LLaMA 3.1) will be straightforward. Unfortunately, this is far from the case.
When switching models, the entire agent can break even when the change appears to be minor. One example involved a switch from LLaMA 3 to LLaMA 3.1—a seemingly small update that caused weeks of re-optimization. The new model behaved differently enough that the existing agentic system could no longer function as intended.
This lack of backward compatibility presents a significant challenge for teams looking to stay on the cutting edge of AI. Changing models requires re-testing and re-optimizing all existing functionality, which adds tremendous cost and complexity to development.
3. Feature Control: The Unseen Problem
Another unexpected challenge in agentic applications is the lack of precise control over the agent’s features and behaviours. Traditional software development is feature-driven: developers define the features, write code, and test them to ensure they work as expected.
In LLMs, however, agents can exhibit behaviours that developers never intended. This can be both a blessing and a curse. For example, an LLM may introduce features like summarization or hate speech detection out of the box—features that developers didn’t explicitly request but which can either be useful or problematic.
As a result, developers must take a more cautious approach to testing. Rather than assuming that the agent will only perform the tasks they’ve programmed, they need to consider the unintended consequences of the agent’s freedom. Test-driven development (TDD) becomes even more critical in this environment, as it helps ensure the system behaves within acceptable parameters.
The complexity of AI-driven features forces developers to be more vigilant, testing for the intended outputs and verifying that the system isn’t generating unintended consequences.
4. The Control Issue: Navigating Agent Alignment
One of the most challenging aspects of building agentic systems is ensuring the agent behaves in accordance with user preferences, developer intentions, and broader ethical standards. The control over an agent’s behaviour exists at multiple levels:
1. Model Provider
The model provider trains the base model, setting the default behaviours and capabilities, such as basic language understanding and safety measures (e.g., preventing harmful outputs).
2. Multisensory AI
The developer who builds the agent can customize the system prompt and design specific behaviours for it.
3. End User
The end user may have preferences for how the agent should behave, such as preferring concise answers over long explanations or having responses delivered in a specific format like markdown.
These layers of control create a complex web of alignment. While the agent builder has some degree of control over the system prompt, the model provider’s training data can still influence how the agent behaves. Meanwhile, user preferences must also be taken into account. This lack of fine-grained control makes it challenging to ensure that agents behave predictably across different contexts.
5. Ethics and the Future of Autonomous Agents
Finally, any discussion about agentic applications would be incomplete without addressing the ethical considerations. Fully autonomous agents that operate without human oversight carry significant risks, ranging from generating biased or harmful content to leaking sensitive data.
Because of these risks, many developers believe that fully autonomous agents are not yet ready for production. Instead, the future may lie in hybrid systems—where parts of the application are agentic, and others are prescriptive or human-supervised. This hybrid approach allows for greater control and mitigates the potential dangers of giving AI agents too much freedom.
Conclusion
Building agentic applications is a thrilling but challenging endeavour. The shift from traditional deterministic workflows to flexible, autonomous systems introduces a new set of operational complexities, especially around backward compatibility, feature control, and ethical alignment. As AI continues to evolve, developers must navigate these challenges carefully to create safe, reliable, and ethically sound systems. The road to fully autonomous AI agents may be longer and more complex than originally thought. However, we can build a future where AI serves humanity’s needs safely and effectively through careful design, rigorous testing, and ethical considerations.
Suppose you’re working in this space or considering implementing agentic applications. In that case, it’s vital to approach these challenges with an open mind and understand that what works in one version of a model might not work in the next. Interested in learning more? Follow our journey as we continue to develop and refine agentic applications in real-world scenarios!
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