Autonomous engineering for AI systems
Sagora compresses weeks of expert engineering into hours, continuously improving the performance, cost, and quality of your AI model pipelines and inference stacks.
AI systems need continuous optimization
- The problem
- Models, workloads, and hardware are ever-changing. Expertise and compute resources are scarce.
- The proof
- Sagora-optimized code is already running in production at multiple S&P 500 companies.
Give Sagora your repo, and it autonomously delivers validated pull requests
Sagora platform
- Input
Code and data
Connect your code, config, and data repos to Sagora.
- 01
Onboarding
Sagora builds a working replica of your system for its agents to experiment.
- 02
Autonomous R&D
Agents propose changes, run experiments, and analyze the results.
- 03
Validation
Agents validate every proposal with stress tests.
- Output
Pull requests
Optimized code with the evidence: benchmarks, experiments, and validation results.
A decade of MIT research, built by the team that did it
Sagora comes out of MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). Professors Mohammad Alizadeh and Hari Balakrishnan spent ten years researching AI-driven systems, the last several with their students building agents that design and optimize real systems. Those agents could do work that once required expert engineers, and with AI systems changing constantly and experts scarce, that work couldn't stay manual. They founded Sagora with two of their former PhD students, Pouya Hamadanian and Arash Nasr-Esfahany, to bring autonomous engineering to every AI system.
- Jeff DeanDiscovery Loop · ex-Google
- Ion StoicaUC Berkeley · Databricks, Anyscale, Athena
- Nick McKeownStanford · ex-Intel · ex-Barefoot Networks
- Aparna ChennapragadaMeta · ex-Microsoft
- Srinivas Narayananex-OpenAI · ex-Meta
- Brian StevensRed Hat · ex-Google Cloud
Frequently asked questions
Who is Sagora for?
Engineering teams building and running AI systems in production:
- ML engineers and scientists developing advanced AI/ML models
- Infrastructure teams serving models at scale, from internal platforms to token factories
- Developers building agentic applications
Sagora improves the performance, cost, and quality metrics each team cares about most:
- Models: lower tail response latency, higher throughput, improved accuracy
- Inference: better throughput per GPU, faster time to first token, lower cost per token served
- Agents: faster responses, higher task success, lower cost per task
How is this different from an AI coding agent?
A coding agent is built to write code. Sagora is purpose-built for systems engineering, so it's more autonomous and gets better results. In our benchmarks, Sagora reached the same results 3–5× faster than leading coding agents and autonomous research tools.
- It sets up its own lab. Sagora mirrors your production environment, with the tools, instrumentation, and guardrails built in. With a coding agent, you build all that yourself.
- It works like a researcher. Sagora finds where the problem actually sits, then forms hypotheses, tests them, and keeps what holds. It breaks goals into sub-goals, optimizes in phases, and will take a step back to find a bigger gain.
- It protects quality. Every proposal is verified and stress-tested, so speed never comes at the cost of quality, and the findings feed back into the whole process.
- It's built for runs that last days. Experiments are fully reproducible, with clear summaries, visualizations, and progress updates on the schedule you choose.
Not convinced? Benchmark Sagora against your favorite coding agent or research tool.
What if I already work with an inference provider?
You don't need to change anything. Your provider runs the serving infrastructure, and Sagora improves the performance, cost, and quality of everything you build on top of it: models, pipelines, agent harnesses, and application code.
If you self-host with frameworks like vLLM or Triton Inference Server, Sagora improves the serving layer too: batching and scheduling, KV cache behavior, CUDA graphs, and custom kernels, alongside everything above it.
Sagora also works directly with inference providers on their own stacks, so improvements can come at both layers.
See what Sagora can do on your system
Tell us what you're running and what you want to improve — performance, cost, or quality.
✓
We've got it
We'll get back to you shortly.