Production tasks are multi-step, stateful, and full of tools your happy-path demo never touches.
AI agent reliability studio
Make agents
ready for reality.
Benchmark, evaluate, improve, and monitor tool-using agents before production turns edge cases into incidents.
Environments and runtimes we ship against
DockerPythonTypeScriptRustGopytestGitHub ActionsKubernetes
01
Your agent works in a demo.
Can it hold up in production?
nnossai turns real workflows, failure modes, and operating constraints into evidence your team can act on.
One invalid action or missed recovery path can erase trust faster than a benchmark score can reveal.
Research, engineering, and safety teams need one view of readiness before release decisions.
What we do
Confidence across
the agent lifecycle.
One technical partner for benchmark design, evaluation, deployment readiness, monitoring, and post-training improvement.
01Measure
AI Agent Benchmarking
Benchmark AI agents on real-world, tool-driven tasks that reflect how agents behave in production.
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We build representative task suites across terminal, browser, API, and enterprise workflows. Each task gets explicit success criteria, replayable fixtures, baseline runs across agent versions, and trace-level failure analysis.
- Tool-use and multi-step task suites
- Baselines, scorecards, and replayable traces
- Failure taxonomy for prioritised fixes
02Verify
AI Agent Evaluation
Test whether an AI agent can reliably complete real-world tasks and handle failures safely.
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We evaluate completion reliability, tool selection, recovery from partial failures, multi-turn consistency, and safety behaviour with deterministic checks and expert review.
- Harness integration and trace capture
- Human, scripted, and policy-based grading
- Clear evidence for release decisions
03Deploy
Enterprise AI Agents
Design, validate, and deploy AI agents that meet enterprise standards for reliability and safety.
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We pressure-test workflows, integrations, permissions, observability, and acceptance criteria before agents reach production users.
- Use-case and workflow design
- Permission boundaries and integration checks
- Deployment readiness and acceptance testing
04Assess
Agent Readiness & Risk Assessment
Get clarity on safety, reliability, and risk before deploying AI agents.
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We map failure modes, data and access risk, action impact, and operational gaps into a practical risk register with go or no-go findings.
- Red-team scenarios and boundary cases
- Risk register with remediation priority
- Executive-ready readiness readout
05Operate
Continuous Monitoring & Regression Testing
Ensure agents do not silently break after deployment with continuous evaluation.
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We maintain version-to-version regression suites, review production traces, detect drift, and create release gates that keep behaviour visible as systems change.
- Scheduled evaluation runs and alerts
- Drift, trend, and regression reporting
- Release gates tied to risk thresholds
06Improve
Supervised Fine-Tuning (SFT) & RLHF
Improve AI agent performance and alignment through SFT and RLHF training.
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We design training tasks, demonstrations, preference data, rubrics, rewards, and hard negatives, then validate gains against the behaviours that matter.
- Demonstration and preference data
- Reward and rubric design
- Post-training evaluation and QA
About nnossai
Evidence for teams
shipping agents.
nnossai is an AI-agent reliability studio. We work with applied-AI teams, enterprise operators, and model builders who need to know what an agent will do when the path is unfamiliar, the tool fails, or the stakes are real.
Our work connects the full lifecycle: define the behaviour, build the environment, run the evaluation, expose the risks, improve the model, and keep checking after launch.
Real workflows
Tasks mirror the systems, permissions, tools, and handoffs your users rely on.
Traceable decisions
Every score ties back to a trace, assertion, failure mode, or review note.
Operational readiness
Outputs help engineering, safety, and leadership make one clear release call.
02
From uncertain behaviour to a clear release decision.
We combine representative environments, observable traces, deterministic tests, and expert review so the result explains what happened and what to do next.
- 01DefineOutcomes, risks, tools, and operating constraints
- 02BuildTasks, fixtures, environments, and graders
- 03EvaluateRun agents and capture complete behavioural traces
- 04ImprovePrioritise failures, training data, and system changes
- 05MonitorProtect releases with continuous regression checks
WORKFLOW / EVIDENCE / DECISION / CONTROL
Built around your production context, not a generic leaderboard.Start where the
risk is highest.
Begin with one meaningful workflow, establish evidence, then expand into the operating model your agent needs.
Focused assessment
A bounded benchmark, evaluation, or readiness review for one agent and one decision.
Most common
Evaluation programme
Ongoing task design, evaluation runs, failure analysis, and release support across agent versions.
Continuous assurance
Scheduled monitoring, regression coverage, trace review, and improvement data after deployment.
Start with evidence
One workflow is enough
to expose what matters.
Select the workflow
Bring the agent, desired outcome, tools, known failures, and release decision.
Run a pilot sprint
We build representative tasks, execute the agent, and capture evidence across success and failure.
Review and act
Receive findings, risk priorities, and a practical path to improve, launch, or monitor.
Can we put an NDA in place first?
Yes. We can agree handling rules before you share workflows, traces, data, or internal systems.
What kinds of agents can you evaluate?
Tool-using agents across browser, terminal, code, API, research, support, and enterprise workflows. The evaluation is shaped around your actual environment.
Do you only provide evaluation?
No. We can support benchmark design, enterprise agent readiness, risk assessment, monitoring, regression testing, SFT, and RLHF data programmes.
How do you define success?
We combine task outcomes, tool behaviour, recovery quality, policy compliance, and business risk into explicit acceptance criteria.
What do we receive?
Depending on scope: task suites, environments, graders, traces, scorecards, failure taxonomy, risk register, improvement priorities, and release recommendations.
What is the smallest way to start?
Choose one important workflow and one release question. We can turn that into a focused pilot with representative scenarios and a clear readout.
Know how your agent
behaves before users do.
Bring one real workflow. We will turn it into evidence, risk priorities, and a stronger path to production.