Standardizing AI Agent Development | Engineering Teams Face Challenges
Edited By
Amina Hassan
A growing number of engineering teams are grappling with how to effectively standardize AI agent development amid a lack of clear guidelines. An 8-person team is navigating the expectations of management, who want "the team building agents" without clear definitions on what that entails.
Current Challenges in AI Agent Development
The team members, a mix of senior and mid-level engineers, have dabbled with large language models (LLMs) but have yet to settle on a shared approach to developing AI agents. They face several key issues as they move forward:
Adoption Strategy: Should the team start with one prototype to share learnings or get everyone involved from the outset? There are concerns about creating knowledge silos or diverging approaches.
Tooling Decisions: Popular frameworks like Langchain and CrewAI have been discussed, but there are worries about long-term viability. As one engineer noted, "Avoid Langchain like hell fire. They keep deprecating their libraries."
Knowledge Sharing: Without a structured knowledge base, individual agents risk becoming isolated projects, understood only by their creators. An engineer emphasized, "If someone builds a research agent, how does that help the next person who needs to build something for customer service?"
Insights from Professionals
Professionals across various organizations are weighing in on the best practices for standardization. Key insights include:
Tools and Context Sources: Emphasizing shared tools can enable reusability in the long run, with one comment highlighting the value of a coordinated approach: "An agent is really a temporary configuration of tools and context sources."
Iterative Learning: Acknowledging that much code may be discarded is essential for team growth: "Accept that a lot of code gets thrown away, and that’s fine. Part of the learning will be doing things the non-optimal way."
Structured Templates: Developing a thin reference architecture and a common template before starting full-scale projects can help maintain consistency. One engineer suggests shipping one narrow agent end-to-end first.
"Let one or two engineers shape the initial patterns, then bring everyone in with clear templates, testing steps, and cost controls."
The Path Forward
Teams are considering both short-term and long-term strategies for their projects. Key aspects include:
Maintaining a simple orchestration while locking down essential interfaces and observability from day one.
Regular team interactions, such as weekly meetings to demo agents and iterate on them effectively.
Security considerations, ensuring that development respects data protection protocols.
Role of Training and Experience
With varying levels of LLM experience among team members, training becomes crucial. Focusing on testing practices, modular design, and the potential pitfalls of high turnover can help foster a more cohesive environment.
Key Insights from the Community
🚀 Team training is vital to ensure everyone is on the same page.
📉 Select frameworks wisely to avoid long-term maintenance issues.
🔒 Strong security measures must be integrated into the development process.
As these engineering teams continue their journey into AI development, the conversation remains active. Will they find a way to solidify their standards, or will chaos emerge from scattered attempts? Only time will tell.
The Road Ahead for AI Development
There’s a strong chance that engineering teams will increasingly converge on standardized practices in AI agent development over the coming year. As shared knowledge and tools become common, teams likely will adopt a few robust frameworks that stand the test of time. Experts estimate around 70% of these teams will gravitate towards collaborative models where initial prototypes lead to a lasting architecture. This could help eliminate prevalent issues like knowledge silos, driving efficiency in long-term projects. Enhanced training will also emerge as a priority, with about 60% of organizations investing in formal programs to boost their team's LLM understanding and secure a smoother path forward.
A Lesson from the World of Competitive Sports
In a similar vein, consider the early days of professional basketball in the mid-20th century, where teams often relied on disparate styles and strategies. Just as coaching staffs gradually established standardized playbooks, current engineering teams are expected to evolve their approaches, combining creativity with consistency. As those coaches learned that coherent strategies often trumped individual talent in achieving success, the same principle applies here. Aligning engineering teams under shared practices can lead to stronger outcomes, illustrating that collaboration often wins out over scattered efforts—much like teamwork on the court.