The AI Gap is no longer about model quality. It’s about Runtime Control

Establish true runtime control by continuously observing behavior, identifying failures, structuring them into actionable signals, and feeding those learnings back to improve your enterprise AI systems.

Try it Now

Contact us

The emergence of runtime assurance represents a structural shift in enterprise security architecture. Enterprises now require mechanisms to ensure that deployed AI systems behave consistently, safely, and in alignment with organizational policies over time.
As AI agents assume increasingly autonomous operational roles, enterprises must ensure that these systems behave reliably, safely, and in alignment with organizational policy and governance requirements. This view is consistent with the feedback we receive every day from CISOs trying to secure their AI ecosystem.
The [Chatsee] platform monitors agent interactions, telemetry, and operational signals to understand how AI systems behave in real-world conditions. This approach enables independent and comprehensive analysis of AI agent performance and risk.

The emergence of runtime assurance represents a structural shift in enterprise security architecture. Enterprises now require mechanisms to ensure that deployed AI systems behave consistently, safely, and in alignment with organizational policies over time.
As AI agents assume increasingly autonomous operational roles, enterprises must ensure that these systems behave reliably, safely, and in alignment with organizational policy and governance requirements. This view is consistent with the feedback we receive every day from CISOs trying to secure their AI ecosystem.
The [Chatsee] platform monitors agent interactions, telemetry, and operational signals to understand how AI systems behave in real-world conditions. This approach enables independent and comprehensive analysis of AI agent performance and risk.

The emergence of runtime assurance represents a structural shift in enterprise security architecture. Enterprises now require mechanisms to ensure that deployed AI systems behave consistently, safely, and in alignment with organizational policies over time.
As AI agents assume increasingly autonomous operational roles, enterprises must ensure that these systems behave reliably, safely, and in alignment with organizational policy and governance requirements. This view is consistent with the feedback we receive every day from CISOs trying to secure their AI ecosystem.
The [Chatsee] platform monitors agent interactions, telemetry, and operational signals to understand how AI systems behave in real-world conditions. This approach enables independent and comprehensive analysis of AI agent performance and risk.

Independent Research Report by TAG · Dr. Edward Amoroso

Download the Research Report

Independent Research Report by TAG · Dr. Edward Amoroso

Download the Research Report

ChatSee.ai named in the Gartner Market Guide for Guardian Agents.

Included in the business alignment category — critical for aligning AI to enterprise goals and governance requirements.

Report

THE PROBLEM

Every agent type has its own way of going wrong.

Your agents don't crash — they drift. And each failure surface looks completely different depending on what the agent was built to do.

AGENT TYPE · 01
Decisioning Agent
Evaluates inputs and assigns outcomes — approve, deny, rank, route. The failure is invisible: same input, different answer, no error raised.
FAILURE TYPES
Policy drift under edge cases
DRIFT
Policy boundaries silently ignored
VIOLATION
Decision logic deviates across geographies
BIAS
The question that matters
Was the assigned outcome correct and consistent given the input data and policy?
AGENT TYPE · 02
Interaction Agent
Handles multi-turn conversations — support, guidance, escalation. Failure is a slow erosion of tone, context, and accuracy across turns.
FAILURE TYPES
Context lost between conversation turns
DECAY
Tone drift eroding brand voice
DRIFT
Unnecessary escalations worsen user experiene
SENTIMENT
The question that matters
Was the interaction accurate, context-aware, and complete across all turns?
AGENT TYPE · 03
Workflow Agent
Triggers system actions — updates, transactions, downstream flows. Failure means incomplete execution chains with no human in the loop to catch them.
FAILURE TYPES
Partial execution — action chain breaks mid-flow
INCOMPLETE
Silent failure — no error, wrong downstream state
SILENT
Handoff friction between agent and systems
FRICTION
The question that matters
Were the intended system actions executed correctly and completely?

Decisioning Agent

Interaction Agent

Workflow Agent

AGENT TYPE · 01

Decisioning Agent

Evaluates inputs and assigns outcomes — approve, deny, rank, route. The failure is invisible: same input, different answer, no error raised.

FAILURE TYPES

Malformed JSON, null tokens, max-3 tags, duplicates

Schema Failure

STRUCT-01

ner_tag not in allowed enum (7 types)

Invalid NER Tag

TAG-01

Decision logic deviates across geographies

GEO-02

The question that matters

Was the assigned outcome correct and consistent given the input data and policy?

Live Decision Trace

txn_8821

$340 · EU

→

Agent

→

APPROVE

txn_8822

$340 · EU

→

Agent

→

DENY

DRIFT

txn_8823

$340 · EU

→

Agent

→

APPROVE

Same input · Different decisions · No alert

Enterprise Layer

Across All Agents

Fragmented governance means agents drift independently — no shared learning, inconsistent policy enforcement, and no unified view across your AI estate.

Policy fragmentation

No shared learning

Compliance blind spots

Siloed observability

The platform

Meet ChatSee Guardian Agent.

The New Layer of Enterprise Control Plane

ChatSee Guardian Agent sits across your agents — monitoring, detecting, structuring, and continuously improving every agent interaction.

Monitor

Unified Telemetry
Execution Traces
Contextual Metadata
Real-time Alerting

Detect

Semantic Drift
Policy Violations
Intent Gaps
Behavioral Anomalies

Structure

Behavioral Taxonomy
Failure Memory
Pattern Discovery
Governance Tagging

Improve

Dynamic Prompt Adaptation
Regression Harness Alignment
Model Improvement Loops
Production Scenarios

Foundation Models
Agent Frameworks
Observability & SIEM
Cloud Platforms
Embedded AIs
Data Platforms

Learn How It Works

What Makes Us Different

Built for the way enterprise AI actually fails.

Generic observability tools weren't designed for agents. ChatSee was built from the ground up for behavioral misalignment — the failure mode that doesn't show up in your logs.

Behavioral Reliability

We go beyond uptime monitoring. ChatSee detects subtle tone drift, persona deviation, and semantic inconsistency — the failures that erode trust long before a ticket is filed.

Consistency scoring across sessions and agent types
Tone and persona drift alerts with severity grading
Automated remediation with audit trail

Learn More

Agent Performance Management

Track agent effectiveness against your actual business KPIs — not just system uptime. See goal alignment, task completion, and downstream business impact in one view.

Business goal alignment scoring per agent
Task attrition and completion rate tracking
Executive-ready reporting dashboards

Learn More

Collaborative Governance

Scale agentic workflows with the confidence of traditional software. Circuit breakers, retry logic, and semantic understanding enable persistent alignment across complex multi-agent systems.

Multi-agent coordination and circuit breaking
Policy enforcement across agent boundaries
Agent owner accountability and audit logging

Learn More

Discovery and Observability

Build a living map of how your agents behave in production — not just what they're supposed to do. Surface emergent patterns, data structures, workflows, unknown failure modes, and optimization opportunities automatically.

Emergent behavior and pattern discovery engine across agents, tools, and data workflows.
Full execution trace replay and comparison
Cross‑agent behavioral and performance benchmarking on real production traffic, including drift, anomaly, and failure‑mode analysis.

Learn More

The Economics

Runtime assurance pays for itself.

Every behavioral incident that reaches a customer is exponentially more expensive than one caught at the runtime layer. ChatSee is the control plane that makes autonomous AI economically viable at scale.