If you're building AI-powered systems in 2026, you've hit this fork in the road: one agent handling everything or a whole crew of specialized bots? Maneshwar, developer behind git-lrc (a micro AI code reviewer that hooks into your git commits), just dropped a deep-dive on exactly this dilemma—and it's worth your time if you're architecting anything serious with LLMs.

The Run Loop: Your Agent's Heartbeat

Every agent runs on what developers call a 'run loop'—essentially a while-loop where the model executes, potentially calls a tool, checks the result, and loops again until it hits an exit condition. Those conditions are usually straightforward: a structured output arrives, an error gets thrown, or you hit max turns. This pattern holds whether you're running one agent or fifty. The only difference at scale is deciding which agent runs on each turn.

Start With One Agent—Seriously

Here's the counterintuitive advice that saves devs the most grief: maximize a single agent before you even think about splitting into multiple. You'd be surprised how far one well-equipped agent takes you. Need new capabilities? Add another tool. That's it. No coordination overhead, no handoff debugging, no 'wait, which agent did that?' moments at 11pm when something breaks in production. Maneshwar's recommendation: use prompt templates with variables instead of maintaining a graveyard of separate prompts. His example is elegant—a call center template with {{company}}, {{user_name}}, and {{tenure}} as variables means new use cases just mean updating values, not rewriting workflows.

When Splitting Actually Makes Sense

Two symptoms tell you it's time to break things apart: prompt complexity spiraling into nested if-this-then-that logic that's getting impossible to maintain, and tool overload where you've got overlapping tools confusing the model despite clear naming efforts. Maneshwar notes some agents handle 15+ well-defined tools just fine while others choke on fewer than 10 that look similar.

Pattern 1: The Manager Approach

One central agent acts as coordinator, calling specialist agents like tools and stitching their outputs into a single response. Think of it as the user's 'one voice' experience. Maneshwar's code example shows a translation manager with spanish_agent, french_agent, and italian_agent passed in as callable tools—clean, composable, easy to trace.

Pattern 2: Decentralized Handoffs

No boss here. Agents are peers, and one can hand off the entire conversation to another along with current state. Perfect for triage flows—a first agent assesses intent and routes to the right specialist who then owns the interaction directly. The example shows a Triage Agent routing between technical_support_agent, sales_assistant_agent, and order_management_agent.

Key Takeaways

  • Use prompt templates with variables instead of separate prompts—this cuts maintenance debt significantly
  • Split when your single agent's prompt becomes an unmaintainable maze or tools start confusing it despite good documentation
  • Manager pattern gives you one unified voice; handoff pattern lets specialists fully own the conversation
  • The same principle applies to both: keep components flexible, composable, and driven by clear prompts

The Bottom Line

The AI agent ecosystem has a bad habit of over-engineering. Before you spin up a multi-agent swarm, ask yourself if you've actually maxed out what one well-tooled agent can do—because the debugging complexity you're adding with multiple agents is real and often unnecessary.