Agents that don't make things up.
CoSarathi for Agents is an SLM-based pipeline for building and running AI agents, engineered for no hallucination by design, at roughly a tenth of the compute cost of a large-model pipeline, with 90%+ task accuracy. This is one of three products built on the CoSarathi platform, see also CoSarathi for Work and CoSarathi for Edge.
Big-model agents are expensive, slow, and confidently wrong.
The cost problem
Every agent call routes through a frontier LLM, even for simple, repetitive tasks, compute bills scale with usage, not with task difficulty.
The hallucination problem
Large models are fluent even when wrong. In production, a confident wrong answer is worse than no answer.
The latency problem
Multi-step agent chains multiply large-model latency at every hop, making real-time agent workflows impractical.
The trust problem
Without a governed pipeline, teams can't explain why an agent did what it did, or prove it won't happen again.
Small models first. Escalate only when needed.
Every task starts on a small language model, purpose-tuned and grounded in your data. A larger model is only called in when the SLM's own confidence check says it should be, most tasks never need one.
Route
Each request is classified and sent to the smallest model capable of handling it, task-tuned, not general-purpose.
Ground & verify
Every answer is checked against retrieved source data before it's returned, no source, no answer, by design.
Escalate on demand
Only low-confidence or high-stakes steps escalate to a larger model, and every escalation is logged and auditable.
Built for production, not demos.
No hallucination, by design
Answers are grounded in retrieved source data and verified before returning, the pipeline is built to refuse rather than guess.
1/10th the computation
Small, task-tuned models handle the vast majority of requests, cutting inference cost roughly 10x versus routing everything through a frontier LLM.
90%+ task accuracy
Task-tuned small models, evaluated continuously against your own benchmarks, not a generic leaderboard score.
Governed by construction
Every escalation, every source, every output is logged and auditable, the same governance model as the rest of CoSarathi.
The numbers behind no hallucination.
Teams shipping their own AI agents.
Product teams
Embed a support, onboarding, or in-app assistant agent without inheriting frontier-model cost or hallucination risk.
Platform & internal tools teams
Automate internal workflows, ticket triage, data lookups, report generation, with an auditable, governed pipeline.
Regulated industries
Deploy agents where an unsourced or wrong answer is a compliance risk, not just a bad customer experience.
From pilot to production, without the guesswork.
Define the task
Scope one or two high-volume agent tasks with clear success criteria and a benchmark dataset.
Tune & ground
We tune the SLM pipeline on your data and connect it to your source systems for grounded answers.
Ship & monitor
Go live with full escalation logging, then expand to more tasks once accuracy is proven.
See CoSarathi for Agents in motion.
Bring us your highest-volume agent task. We'll show you the accuracy and cost difference on your own data.
Two more ways to run CoSarathi.
Need to get more work done across your team? That's CoSarathi for Work. Need eyes on your physical sites? That's CoSarathi for Edge, same platform, same governance model.