Is More AI Agents Better? The Paradox of Vibe Coding Learned by Reducing from 22 to 17
Based on the experience of rightsizing AI agents recklessly increased through vibe coding from 22 to 17, we analyze the principles of agent count optimization and the productivity paradox.
Blindly increasing AI agents through vibe coding actually reduces development productivity. In practice, while consolidating from 22 agents down to 17, I experienced firsthand a clear paradox between agent count and maintenance burden. The key lesson from this rightsizing is that a smaller number of well-designed agents enhances the overall system's stability and speed.
The Trap of Indiscriminate AI Agent Expansion
The appeal of vibe coding lies in rapid prototyping and automation. Since AI agents can be easily created and connected, it's easy to fall into the initial mindset that "more agents can automate more tasks." However, in reality, as interactions between agents increase exponentially, unexpected side effects begin to emerge. For instance, different agents accessing the same resource may collide, or the output of one agent feeding into another can cause cascading errors more frequently. According to a recent report by Newsweek (July 2026), as AI agents become more autonomous, the cost of controlling and coordinating them within organizations is skyrocketing. This is not simply because of the number of agents, but because when agents' responsibilities become ambiguous and interdependencies deepen, the predictability of the entire system declines.
Furthermore, as Business Insider analyzed in June 2026, as the culture of not reviewing vibe-coded code by humans spreads, quality control of agents becomes even more difficult. Even though each agent appears to operate independently, in reality, they are entangled with numerous internal calls and data dependencies. When problems arise, identifying the root cause agent is not easy. Ultimately, indiscriminate expansion has the opposite effect of increasing debugging and maintenance time rather than speeding up development.
From 22 to 17: The Actual Process of Rightsizing
When initially operating the 22 agents, I thought each agent was handling a clear, single task. Over time, however, I discovered that the functions of several agents overlapped, and a significant number of agents were barely called. Rightsizing was not simply an act of deleting five agents, but a process of redesigning the entire system. First, I analyzed the usage frequency and dependency graph of all agents. The result showed that out of 22, 8 agents were partially duplicating the functions of other agents, and 3 had never been used in actual workflows. Based on the remaining 11, through integration and redistribution, I finally configured 17 agents.
The most challenging part of this process was redefining the interfaces between agents. For example, when combining two agents that handled "data cleansing" and "data transformation" into a single "data preparation" agent, all other agents that depended on these two had to have their calling methods modified. However, once this structure stabilized, the system's response time improved by an average of over 20% and error rates were halved. This was because unnecessary network hops and data serialization costs were eliminated. It was not a simple downsizing, but rather making agents' responsibilities larger and clearer, reducing complexity.
Agent Optimization Principles in a Vibe Coding Environment
Based on this experience, I derived several principles for optimizing the number of agents in vibe coding. First, do not map one business function to one agent; instead, allocate a self-contained "domain." Splitting agents into small units according to technical convenience quickly leads to sprawl. Instead, they should be grouped into meaningful units from an external perspective, such as "user authentication," "order processing," and "notification delivery." Second, minimize communication between agents. Use event-driven asynchronous messages rather than direct calls, and avoid shared state as much as possible. Third, establish a process to periodically decommission unused agents. Like microservices, agents tend to be neglected over time. Fourth, rightsizing should become an ongoing activity, not a one-time event.
As pointed out in a July 2026 article by Dark Reading, AI agents should be recognized as a new kind of "identity." In other words, unless each agent's permissions and responsibilities are clearly defined, the security and governance of the entire system can collapse. For agents that rapidly increase through vibe coding, it is important to apply a scalable governance framework from the start.
The Trade-off Between Expansion and Maintenance
Increasing the number of agents creates the illusion that more tasks can be processed in parallel. In reality, the coordination cost between agents does not increase linearly, but proportionally to the square of the number of connections. The potential connections for 22 agents is 231, but when reduced to 17, it drops to 136, a reduction of over 40%. This is not just a number; it directly relates to the likelihood of failure propagation and debugging complexity.
Furthermore, according to a 2026 forecast by Telecoms, AI agents are predicted to outnumber human users in future mobile networks. In an environment where agents multiply exponentially, the manageability of the entire ecosystem becomes a more critical competitive factor than the efficiency of individual agents. Thus, while rapid development through vibe coding is important, it is necessary to beware of overly granular agent design from the beginning for sustainable operations.
Conclusion: An Agent Ecosystem Where Less is More
The process of reducing from 22 to 17 was not just a numbers game, but an effort to balance the speed of vibe coding with operational stability. The stereotype that more agents are better is bound to break in reality. A smaller number of well-defined agents provides more reliable automation. An environment where humans can review and adjust the code and agents created by AI remains crucial. In this regard, tools like md-log, which help people comfortably review and version-control AI outputs, practically contribute to establishing a genuine "human-in-the-loop" review culture amidst the chaos of vibe coding. Going forward, I will not stop questioning whether a new agent is truly necessary before adding it.
References
- The Empty Office: What Happens When AI Runs the Company - Newsweek
- AI Agents Are a New Kind of Identity & Most Orgs Aren't Ready - Dark Reading
- AI writes a lot of software. Now, human code review is starting to disappear. - Business Insider Africa
- New AI Agents Pose ‘Existential Threat’ to How Grants Are Awarded - Inside Higher Ed
- Will AI agents drive the next decade of mobile network growth? - Telecoms
- What Happens When AI Agents Stop Asking and Start Acting? - Newsweek
- Qualcomm Says Agents Will Decide Where AI Runs - Newsweek
- AWS Kills The AI Services It Launched Just Two Years Ago - Forbes
- Meta CEO says agent development not accelerating as expected: report - Seeking Alpha
- Industries Expand Hiring for AI Coding Roles Amid Widespread Adoption - Hotel News Resource
- OpenClaw Matures Amid Swarm Culture - Forbes
- Operationalizing Agentic AI: from assisted to autonomous - csoonline.com
Frequently asked questions
- Is unconditionally reducing the number of AI agents the right answer?
- No. Rather than simply reducing the count, the key is to clarify the scope of responsibilities for each agent, consolidate overlapping functions, and remove unused agents. Even if the number is small, problems can still arise if roles are ambiguous.
- How many agents should be maintained in vibe coding?
- There is no fixed number, but it is advisable to design each agent to handle a complete business domain. Typically, managing around 10 to 20 agents is considered manageable, but it varies depending on team size and system complexity.
- What is the most important criterion when rightsizing?
- The top priority is to analyze dependencies and call frequency between agents to identify those that are actually unused or tightly coupled with others. Afterwards, when consolidating or redesigning, you should simplify agent interfaces and reduce communication overhead.
- Won't reducing the number of agents slow down development speed?
- On the contrary, unnecessary coordination costs decrease and debugging time is shortened, leading to improved development speed in the long run. In the short term, integration takes time, but once the overall system's predictability and stability increase, rapid deployment becomes possible.
- How can we ensure the quality of agents created with vibe coding?
- Regular reviews and rightsizing iterations are necessary. Additionally, it is important to maintain a process where humans review AI-generated code and to establish logging and alerting systems to monitor agent behavior. Using tools like md-log to track change history allows you to quickly correct mistakes.