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HaliosAI

AI Agents break in subtle ways. HaliosAI provides runtime guardrails and continuous evaluations to keep AI agents on-course and resilient, so your teams ship faster with confidence.

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Why It Matters

AI agents do more than just chat - they act. They call APIs, reason over state, chain multiple steps, and evolve with use. Without oversight, this autonomy can silently misalign:
  • Agents can hallucinate, misconstrue context, or drift from intended behavior
  • Minor errors may cascade into system-level failures
  • Misactions may violate policies, leak data, or trigger compliance failures
  • Poorly detected drift increases maintenance, debugging, and rollback cost
  • Consequences are real: loss of trust and brand reputation, finacial laibilities and security/compliance incidents.
In practice, teams deploying agents have faced unexpected outcomes - erroneous policy recommendations, data corruption, unauthorized actions, users manipulating agent behaviour. This is because initial performance during development doesn’t guarantee stable behavior in production under evolving conditions. HaliosAI injects guardrails and evaluations into your agent layer so failures are caught early, drift is surfaced continuously, and your teams retain control over the behavior evolution of your agents.

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What HaliosAI Does

HaliosAI solves this by combining two core capabilities:

Runtime Guardrails

Intercepts and validates agent decisions in real time, preventing risky or misaligned input or outputs before they reach LLM, users or downstream systems.

Continuous Evaluations

Runs offline and live assessments of your agent’s behavior-tracking accuracy, consistency, tool usage, drift, and failure modes over time.
Together, these allow you to detect, block, and correct failures-not after a crash, but proactively.

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How Does It Work?

  • You wrap your agent call (e.g. via a Python decorator or API). This single decorator wraps your logic with protection and evaluation, while leaving your core agent code unchanged.
import asyncio
from haliosai import guarded_chat_completion

@guarded_chat_completion(agent_id="your-agent-id")
async def my_ai_function(messages):
    # Your LLM call here
    return await openai_client.chat.completions.create(
        model="gpt-4",
        messages=messages
    )

# That's it! Your function is now protected by guardrails
response = await my_ai_function([
    {"role": "user", "content": "Hello, how can you help me?"}
])
  • When an agent executes, HaliosAI runs guardrail logic alongside the model, validating or suppressing risky requests, responses and tool calls.
  • All agent interactions (model output, tool calls, context) are captured and scored.
  • Periodic evaluation runs deeper checks—testing accuracy, drift, rule adherence, and behavioral regressions.
  • You get visibility: dashboards, alerts, logs, and reports to diagnose and tune.

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Unified Agent Behavior Insights

Dashboard

Dashboard

View Runtime Checks

View Runtime Checks

Guardrails and Policies

Guardrails and Policies

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Key Features

  • Offline & live guardrail checks
  • Streaming validation of large / multi-part outputs
  • Parallel execution (guardrail + model) to minimize latency
  • Multi-agent support & per-agent configurations
  • Python SDK + REST API for easy integration
  • Monitoring dashboards, alerts, and drift detection

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Explore Next

Quick Start

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Use Cases

Explore how HaliosAI works across the agent lifecycle

Explore UI

Learn the complete workflow from signup to monitoring

Integration Examples

Practical integration examples