Cohesor

Cohesor is a neutral control plane for enterprise AI agents. It sits between your agents and the LLMs they call, handling routing, compression, policy enforcement, and observability. Instead of pointing each agent at a different model endpoint, you point them all at a single Cohesor URL. The gateway then decides which model to use for each request, compresses prompts before they're billed, and enforces budgets and rate limits — all in about 41 milliseconds. The result is a 60–90% reduction in AI costs without changing your agents' behavior.

How does it work? Every request passes through five checkpoints: Ingress (single endpoint, native Anthropic/OpenAI protocols), Policy (key validation, team scopes, budget checks), Compress (lossless token reduction using stale-read supersession, context compaction, semantic dedup, and structural rewrite), Route (a lightweight classifier scores each prompt and sends it to the right-sized model — strong for hard problems, economy for the rest), and Observe (full traces, per-user spend, LLM routing decisions, and audit logs). You can set routing objectives — quality, balanced, cheapest, or fastest — and Cohesor dynamically splits traffic across models like GPT-5, Claude Opus 4.6, Gemini 2.5 Pro, and Llama 70B on Groq. The token compression alone cuts input tokens by about half on average, with a semantic similarity floor of 0.97 to ensure context stays intact.

Cohesor is built for engineering teams and enterprises that run multiple AI agents — support bots, coding assistants, research agents, checkout flows — and want to control costs without sacrificing quality. It's especially useful for teams that need per-user spend attribution, hard budget caps, and showback reports for finance. If you're managing a growing fleet of AI agents and your LLM bill is climbing, Cohesor gives you a single pane of glass to route, optimize, and govern every request.