How Halios keeps your agents reliable at runtime.
Halios sits between your agents and production. It captures live behavior, evaluates every interaction across structured dimensions, and feeds clear signals back into your development workflow.
The Halios Loop
Capture. Evaluate. Improve. Repeat.
This is the core of the Halios platform. Every agent interaction moves through four stages, and the cycle never stops.
Monitor
Automatically capture agent traces, tool calls, and model responses from production or CI.
Raw behavioral data from every interaction.
Evaluate
Score every interaction against deterministic rules and LLM-as-a-judge rubrics tuned to your domain.
Evidence-based scores and insights
Optimize
Surface failed traces and low-scoring patterns, then route findings to reviewers or prompt refinement.
Improvements backed by production evidence, not guesswork.
Scale
Ship updates with regression checks and clear before-and-after comparisons against your baseline.
Faster releases with measurable confidence.
Integration
Fits your stack.
Not the other way around.
Halios meets you where you are. Choose the integration that fits your architecture. All three feed the same evaluation loop.
Native SDK
Drop our Python SDK into your existing agent code. One decorator captures the full conversation trace. Instrument your first workflow in under 20 minutes.
Gateway Proxy
Point your LLM traffic through the Halios gateway. Zero code changes to your agent. Full trace capture and real-time evaluation at the network layer.
CI/CD Integration
Trigger evaluations on every commit. Compare prompt, model, or tool changes against your golden trace library before merging.
Import existing traces
Import existing traces from your environment. Works with Langsmith, Weights & Biases, and other trace providers.
All integration modes run inside your environment. No data leaves your infrastructure.
Evaluation dimensions
Six dimensions.
Every interaction.
We don't just check if the agent responded. We evaluate whether the response was correct, safe, compliant, and useful across six structured dimensions.
Task Completion
Did the agent accomplish the user's stated objective? Is the output actionable and complete?
Safety & Compliance
Does the response adhere to organizational policies? Are there PII leaks, hallucinated commitments, or unauthorized actions?
Tool Usage
Did the agent call the right tools, in the right order, with the right parameters? Were there unnecessary or unauthorized tool invocations?
Reasoning Quality
Is the agent's reasoning logically sound? Are intermediate steps consistent with the final output?
Response Format
Does the output match the expected structure, length, and tone for this workflow?
Policy Adherence
Does the response respect the business rules, pricing constraints, and escalation policies you've defined?
Deployment
Straightforward
container deployment.
Halios ships as a container. Point it at your agent traffic, apply your evaluation config, and start capturing traces. Results arrive as soon as the first trace is available.
Container deployment
Docker containers. No managed infrastructure or external dependencies to provision.
Runs anywhere
VPC, private cloud, on-prem, or air-gapped environments. Wherever your agents live, Halios runs alongside them.
OTel native
Export traces and metrics to your existing observability stack without replacing what already works.
Install model
Containerized and self-hosted.
Data boundary
Runs inside your VPC, private cloud, or on-prem environment.
Observability
Exports traces and metrics into your existing stack.
Activation
Useful signals begin as soon as live traces start flowing.
Works with your stack
Framework-agnostic by design.
If your agent makes LLM calls, Halios can evaluate them.
See the loop
in action.
We'll instrument one of your workflows and show you exactly what the evaluation loop surfaces, in your environment, with your data.