Traigent provides an Agent Optimization Platform that automatically evolves and fine‑tunes coding agents such as Claude Code, Cursor, or Codex to achieve better outcomes while lowering costs. By analyzing only KPI scores and configuration parameters, it can reduce LLM expenses by up to 60% and cut engineering time by up to eight weeks, all without exposing proprietary data. The service iteratively optimizes agents across data, evaluation, search, and guardrail pillars to maximize performance and confidence.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises using coding agents such as Claude Code, Cursor, or Codex face high large‑language‑model (LLM) costs and unpredictable performance, making it difficult to guarantee accuracy, speed, and compliance while maintaining engineering productivity.
Solution
Traigent offers an agent optimization platform that continuously evolves a company’s coding agents to improve key performance indicators (KPIs) such as accuracy, speed, and cost efficiency. The system reconstructs datasets from execution logs, creates calibrated evaluation scorers, and runs automated, multi‑dimensional searches across thousands of configuration variants to locate Pareto‑optimal settings. By iterating through assess‑score‑recommend‑improve cycles, Traigent can reduce LLM expenses by up to 60% and reclaim up to eight weeks of engineering time, while delivering 100% confidence in shipped code. The architecture keeps all proprietary data on‑premise; only KPI scores and configuration metadata are sent to the cloud, preserving privacy.
Target Audience
Primary customers are software development organizations and enterprise engineering teams that deploy AI‑powered coding assistants and need to control LLM costs, improve code quality, and meet regulatory or corporate compliance standards.
Features
- Automated dataset reconstruction from agent logs to ensure comprehensive coverage and discrimination
- Dynamic evaluation engine that manufactures or calibrates verifiers to reliably rank agent configurations
- Scalable multi‑objective search across 25+ configuration dimensions, evaluating millions of variants without brute force
- Pareto‑frontier identification that balances cost, latency, accuracy, and other KPIs
- Privacy‑preserving design: all sensitive code, logs, and guidelines remain on‑premise, with only scores and config metadata transmitted
- Continuous re‑optimization that automatically re‑runs when underlying conditions or requirements change
- Integrated observability dashboards for KPI tracking and configuration recommendations