Leaders of AI vs LiteLLM

Team of 10 Manages 50+ AI Agents with New Hierarchy

A Berlin AI academy, Leaders of AI, found that after deploying more than 50 AI agents the ten-person human team spent most of its time coordinating the agents rather than doing core work. The organisation — which had capped human full-time staff at ten when it began in January 2024 — introduced a new agent hierarchy: a team‑leader agent to coordinate work and an analysis agent that autonomously adjusts other agents' instructions based on performance data. The article notes broader adoption of AI in German businesses, citing a Bitkom survey that reported AI use rising from 17% to 41% within one year. The deeper 'blueprint' use case details are provided behind t3n PRO subscription.

• Leaders of AI (a Berlin AI academy) started in January 2024 with a rule of no more than ten human full-time employees.
• The organisation ran more than 50 AI agents, which caused the ten human staff to spend most of their time coordinating and directing agents.
• Leaders of AI implemented an agent hierarchy: a team-leader agent for coordination and an analysis-agent that adjusts other agents' instructions using performance data.

Team Adds Digital Boss After 50+ AI Agents

A t3n PRO use case describes how Berlin-based Leaders of AI scaled from a human-first rule to running over 50 AI agents, which caused ten human team members to spend more time managing agents than doing productive work. The organisation — founded as a Berlin AI academy in January 2024 with a self-imposed cap of ten full-time humans — introduced a new agent hierarchy: a teamleader-agent to coordinate work and an analysis-agent that autonomously adjusts other agents' instructions based on performance data. The article cites a Bitkom survey showing AI adoption rising from 17% to 41% in one year and outlines that the solution was organisational (hierarchy and briefings) rather than merely technical.

• Leaders of AI (a Berlin AI academy) started in January 2024 with a rule of no more than ten full-time human employees; agents would perform the rest.
• The Leaders of AI team exceeded 50 AI agents, which forced the humans to spend substantial time coordinating and managing the agents.
• The team implemented a new agent hierarchy including a teamleader-agent for coordination and an analysis-agent that adapts other agents' instructions based on performance data.

Auto Router: 45% Lower Cost on 25 SWE-bench Tasks

We solved 23 of 25 SWE-bench Verified tasks with LiteLLM's experimental capability router for $11.15, compared with $20.27 using Opus 5.

Introducing LiteLLM Fusion: 56% More Tasks Solved Than Fable 5

LiteLLM Auto Router Fusion ran three models on the same task and synthesized their work, solving 14 of 21 Terminal-Bench tasks against 9 for Claude Fable-5 alone. Total spend rose 36%, cost per solved task fell 12%, and turn latency went up 5x.

Configure LiteLLM as Codex Model Provider

A developer guide demonstrating how to route Codex to use LiteLLM as a custom model provider. The post explains exposing a LiteLLM API key as an OS environment variable, updating Codex's .codex/config.toml to set model_provider to 'litellm' and add provider-specific fields (base_url, env_key, wire_api, streaming options), setting optional custom HTTP headers, and noting that session models are fixed at session creation. The author also advises verifying usage via LiteLLM dashboard logs and links to LiteLLM and Codex documentation for reference.

• Author Julia Shevchenko published a how-to on dev.to on 2026-08-28 about configuring LiteLLM as a gateway for Codex.
• LiteLLM exposes an OpenAI-compatible interface and can act as a gateway for LLMs.
• Required steps include setting LITELLM_API_KEY as an OS environment variable and updating .codex/config.toml to set model_provider = "litellm" and provider-specific settings (base_url, env_key, wire_api, streaming options).