Monitor, Evaluate, & Test AI Agents in Production with Observability

Enhance your AI agent performance, streamline workflows, and drive high-quality outcomes by tracking key insights across agent behavior, performance, and interactions.

Create an Observability Dashboard

Resolve issues faster for complex, multi-step workflows.

Built-in monitoring helps you get complete visibility into the agentic ecosystem by reducing debugging time from hours to minutes, helping you spot errors, identify root causes, and track configuration changes all in one place.

Improve your agent performance and reduce costs.

Track latency, token usage, error rates, and model or tool performance to optimize workflows and reduce unnecessary system costs.

Create transparent and auditable agentic AI systems.

Keep a clear record of every input, output, and audit trail of every decision an agent makes, ensuring compliance with regulatory standards.

Our End-to-End Range of Agentic AI Observability Services Across Different Platforms and Environments

At Trigma, we provide governed agentic AI systems by giving you end-to-end visibility into not just how AI agents and LLMs are running, but tracing them at every step from initial request to final response.

Observability Strategy and Consulting

We help enterprises design a strong foundation for AI agents by assessing monitoring readiness, identifying visibility gaps, and building scalable strategies that support long-term growth and operational reliability.

Assess your AI ecosystem to measure observability maturity, find blind spots, and identify gaps in monitoring, tracing, and operational visibility.
Design observability architecture, recommend the right tools and frameworks, and create an implementation roadmap that guides your AI agents from pilot deployments to production scale.

Instrumentation and Implementation

We implement observability directly into your AI tech stack to provide end-to-end visibility across agents, workflows, and model interactions.

Configure OpenTelemetry, custom SDK instrumentation, traces, and MELT pipelines to capture metrics, logs, and events across AI infrastructure.
Integrate observability with frameworks such as LangChain, LangGraph, Crew AI, and AutoGen for unified monitoring.

AI Agent Performance Monitoring

We monitor your agent performance across workflows by tracking system health, resource usage, and execution behavior to help teams reduce failures and optimize agent performance.

Monitor response latency, inference speed, error rates, and system resources to maintain stable and efficient AI operations.
Track token consumption, API calls, and cost per interaction, enabling detailed cost attribution per agent and better visibility into overall AI spending.

Behavioral and Decision Observability

We give you clear visibility into how agents think, make decisions, and execute tasks, so you can understand their behavior, catch issues early, and ensure that agents deliver consistent outcomes.

Break down the step-by-step reasoning an agent makes during task execution to ensure it follows correct logic.
Identify when an agent’s behavior changes over time due to data, model, or environmental shifts.

Output Quality and Evaluation

As part of our agent observability services, we help you measure and improve the quality of AI-generated outputs so they remain accurate, relevant, and reliable for real-world use cases.

Build test datasets from real user interactions and system logs for evaluation.
Evaluate how well the retrieval system fetches relevant data, how fast it responds, and how efficiently the data store performs.

Security and Guardrail Monitoring

We help you secure your AI systems by implementing guardrails and continuously monitoring for compliant and controlled AI operations.

Detect prompt injection and jailbreak attempts to bypass system controls.
Implement role-based access control, AI gateways, and data masking for enhanced security.

Multi-Agent System Observability

We help you monitor and optimize multi-agent ecosystems by providing complete visibility into how agents interact, collaborate, and execute complex workflows.

Implement cross-agent tracing to follow the task flow between multiple agents.
Monitor supervisor and specialized agents for performance and decision accuracy.

Infrastructure and Operational Observability

We help you manage the complete AI infrastructure stack so your systems run reliably and scale without disruptions.

Keep a close watch on the orchestration layer that coordinates different AI components, along with AI dependencies, to identify failures.
End-to-end pipeline observability gives you complete visibility into how data flows through the system, from input to output.

Dashboard and Reporting

We help you turn complex AI system data into clear, actionable insights through intuitive dashboards and real-time reporting.

Design and set up custom observability dashboards using tools such as Grafana.
Create compliance and audit dashboards for governance and regulatory needs.

Managed Observability Services

We manage your entire observability stack, ensuring all tools, integrations, and data pipelines are running smoothly.

Deliver monthly observability health reports that provide clear insights into system performance and improvement areas.
Conduct regular architecture reviews to ensure the AI agent evolves with changing business needs.

Training and Enablement

We train your teams in the skills and expertise needed to handle agent performance, observability best practices, governance frameworks, and policies.

Conduct hands-on workshops on observability tools such as Langfuse, Grafana, Prometheus, and Datadog.
Upskill in-house teams on AI agent monitoring and performance tracking.

Success Stories On Building Governed Agentic AI Systems

We’ve helped 240+ businesses through centralized governance and intelligence platforms that moved them from zero visibility to complete accountability.

We don’t just measure what an agent is doing and how it’s performing; we measure agentic AI performance in terms of calculating ROI, optimizing AI usage, and whether it’s scalable enough to handle high-performance workloads.

AI Governance and Intelligence Platform

Challenge

The enterprise is using AI tools such as ChatGPT, Copilot, and agents, but cannot measure ROI and performance. There's no clear visibility into what AI is actually doing and no accountability for the actions AI agents make.

Solution

Our tech engineering team created an AI workforce intelligence platform by tracking AI actions, measuring the performance of AI outputs, and comparing human vs. AI work to evaluate efficiency.

Impact

Clear ROI tracking and cost optimization Full visibility into AI operations and decisions

Build Your AI Solution

AI Workforce Operating System

Challenge

Enterprise AI projects fail to scale due to a lack of orchestration, governance, and control mechanisms, and the absence of human oversight in AI workflows.

Solution

We developed an enterprise AI control plane, which is an infrastructure layer for managing AI-driven operations. This platform includes multi-agent orchestration, governance controls, and human-in-the-loop workflows.

Impact

ROI visibility Reduced risk through governance and control mechanisms Successful scaling of AI initiatives across the enterprise

Build Your AI Solution

AI-Powered Event Platform

Challenge

Event discovery is fragmented, lacks real-time insights, has low engagement, and offers limited monetization opportunities for viewers and sponsors.

Solution

Our AI developers created an AI-powered, real-time event discovery and engagement platform that includes admin controls and a built-in monetization engine for sponsors.

Impact

Simplifies event discovery Boosts user engagement

Build Your AI Solution

Why Startups and Growing Enterprises Choose Trigma For AI Observability Services

At Trigma, we provide end-to-end observability services for enterprises from assessment to architecture design to deployment and continuous optimization.

With built-in governance frameworks and hundreds of integrations, we help you achieve your business outcomes no matter how complex your tech stack is.

Discovery and Agent Audit

We assess your existing AI agent landscape where they’re deployed, which frameworks power them (LangChain, Crew AI, AutoGen, GPT-based), what LLMs they use, and what monitoring gaps exist.

Then, we map each AI agent, its workflows, tool dependencies, and data flows.

Observability and Architecture Design

We design a tailored observability stack by selecting the right mix of tools such as Langfuse, LiteLLM, Prometheus, Grafana, Datadog, or Splunk based on your environment, scale, and compliance needs.

We define what to monitor, how data flows, and how alerts and reporting will work.

Instrumentation and Implementation

We embed observability into your tech stack using OpenTelemetry. This means whenever an agent runs, makes a decision, calls a tool, or interacts with another agent, it gets recorded.

Dashboard and Alert Configuration

We create a dashboard (observability command center) using tools such as Grafana for live performance tracking, Prometheus for metrics, and Langfuse for trace and quality visibility, and set up reporting dashboards in Power BI or Tableau.

Testing and Validation

Before going live, we run controlled tests to make sure everything works correctly.

This includes validating trace completeness, cost attribution accuracy, alert sensitivity, human-in-the-loop escalation triggers, and dashboard accuracy against actual agent behavior.

Go Live and HyperCare

We deploy the agentic AI systems into production and closely monitor them during the initial phase. We resolve early issues, fine-tune thresholds based on real traffic, and ensure the system stabilizes before full handoff.

Training and Knowledge Transfer

We train your internal teams on tools like Langfuse, Grafana, Prometheus, and your dashboards. We also provide complete documentation, runbooks, and hands-on walkthroughs so your team can operate the system independently.

Ongoing Optimization and Managed Services

We provide 24/7 managed services, including continuous monitoring, monthly health reports, new agent onboarding, cost optimization, architecture reviews, and SLA-backed incident response to ensure that your AI agents run smoothly.

More Reasons Why 100+ Companies Choose Trigma For Multi-Agent System Observability

At Trigma, we provide AI governance and monitoring solutions for enterprises so they can get detailed visibility into AI agent activity and their teams, helping teams monitor every aspect of AI agents and keep the platform fully auditable.

Build Proprietary Products

Trigma doesn’t just use third-party tools; it has its own proprietary products like AI workforce intelligence and governance platforms built with observability in mind.

Guarantees ROI and Business Accountability

Most observability providers focus on what the agent is doing. Trigma goes further by showing “Is the agent worth it or not?”

This includes ROI tracking, human-to-agent ratio monitoring, cost-per-workflow analysis, and measuring business impact.

Full Stack Observability

From initial assessment to architecture design, integration, dashboards, testing, training, and ongoing managed services, Trigma covers the entire observability lifecycle.

While most AI agent development companies focus on a single layer, Trigma delivers end-to-end coverage.

Multi-Agent Expertise

We build observability platforms for multi-agent systems, not single-agent setups. This includes distributed tracing, cross-agent communication monitoring, coordination failure detection, and prevention of cascading failures from the start.

Enterprise-Grade Tech Stack

Trigma’s observability stack includes Langfuse, LiteLLM, OpenTelemetry, Prometheus, Grafana, ClickHouse, and more, giving enterprises flexibility through seamless integration with existing infrastructure systems.

Regulated Industry Ready

At Trigma, we build observability solutions with compliance in mind, supporting standards such as GDPR, HIPAA, SOC 2, SR 11-7, and NAIC.

Features like audit trails, PII/PHI detection, data masking, and evidence generation are built into every deployment.

The Tech Stack We Use For Powering AI Observability Solutions

Each technology we select, such as Langfuse, LiteLLM, OpenTelemetry, and others, not only captures every AI interaction but also helps you measure performance, control risk, and maximize ROI at scale.

GPT-5.2

Claude 4.5

Gemini 3.2

Llama 4

Mistral Large

Deepseek

LangGraph

CrewAI

Microsoft AutoGen

LangChain

LlamaIndex

FlowiseAI

PyTorch

TensorFlow

JAX

Pinecone

Weaviate Cloud

MongoDB Atlas Vector Search

Qdrant

Milvus

ChromaDB

PGvector (PostgreSQL)

AWS Bedrock

Microsoft Azure AI Studio

Google Vertex AI

NVIDIA NIM

vLLM, Hugging Face TGI

Ollama

NVIDIA DGX Cloud

AWS Inferentia/Trainium

NVIDIA NeMo Guardrails

Guardrails AI

LangSmith

Arize Phoenix

MLflow

Weights & Biases

Frequently Asked Questions

What does AI observability mean?

AI observability is the process of capturing, analyzing, and connecting data across your technology stack to understand how AI systems operate in a production environment. It provides real-time visibility into LLMs, AI agents, orchestration layers, and their impact on applications and infrastructure.

Why is AI observability essential?

AI observability is essential for identifying performance issues, reducing bias, and maintaining transparency. It allows businesses to scale AI adoption while ensuring trust, reliability, and performance.

Which features should you consider for an AI observability platform?

An AI observability platform should offer capabilities like metric tracking, visualization, data segmentation, bias detection, root cause analysis, and real-time alerting.

How does AI observability differ from the AI control plane?

AI observability focuses on monitoring and understanding what AI agents are doing, while an AI control plane goes further by combining visibility with governance and policy enforcement. It takes a step further by blocking malicious inputs, preventing harmful outputs before they occur, and enabling human approvals for high-risk decisions in real time.

In what ways does AI observability enhance model performance and reliability?

By continuously tracking model behavior, AI observability detects errors, biases, and drift at an early stage. This enables teams to quickly pinpoint improvement areas and make timely adjustments.

Can AI observability integrate with an existing AI and data stack?

Yes, we integrate AI observability solutions with the existing tech stack, such as LLMs, ML pipelines, data platforms, and enterprise systems, without major disruption.

How quickly can we implement AI observability?

Implementation timelines vary, but most organizations see value within weeks through phased deployment and integration.

Do I need in-house expertise to implement AI observability tools?

Not necessarily. Most platforms we design are built for both business and technical users, with dashboards and insights that are easy to interpret.

What industries benefit most from AI observability?

Industries with high compliance, scale, and customer impact, such as finance, healthcare, retail, and telecom, benefit significantly.

What kind of visibility will we get into our AI systems?

You get visibility into AI inputs, outputs, decisions, performance metrics, and user interactions across workflows.