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Karyam

Observability Overview

Building AI systems is easy.

Operating them in production is hard.

Traditional AI applications often behave like black boxes:

  • Why did the AI make this decision?
  • Which documents influenced the answer?
  • Which tools were executed?
  • How much did the request cost?
  • Why did the workflow fail?
  • Which model generated the response?

Without visibility, debugging and governance become nearly impossible.

Why Observability Matters

Section titled “Why Observability Matters”

Production AI systems require the same engineering discipline as modern software systems.

Organizations need to understand:

  • What happened
  • Why it happened
  • When it happened
  • Who initiated it
  • Which systems were involved
  • How much it cost

Observability transforms AI from an unpredictable black box into an auditable and manageable system.

The Problem with AI Black Boxes

Section titled “The Problem with AI Black Boxes”

Traditional AI systems often look like this:

User Request
      ↓
AI Magic
      ↓
Response

When something goes wrong:

  • There is no visibility into the reasoning process.
  • Tool executions are hidden.
  • Retrieval behavior cannot be inspected.
  • Costs are difficult to control.
  • Failures are difficult to diagnose.

The Karyam Approach

Section titled “The Karyam Approach”

Karyam provides end-to-end observability across the entire AI execution lifecycle.

User Request
      ↓
Agent Reasoning
      ↓
Knowledge Retrieval
      ↓
Skill Execution
      ↓
MCP Tool Calls
      ↓
Workflow Decisions
      ↓
Human Approvals
      ↓
Response

Every step is visible.

Every action is traceable.

Every decision is auditable.

Observability Pillars

Section titled “Observability Pillars”

Karyam observability is built around several core pillars.

Agent Logs

Section titled “Agent Logs”

Track every interaction between users and AI agents.

Examples include:

  • User messages
  • Agent responses
  • Tool invocations
  • Execution steps
  • Model decisions

Workflow Logs

Section titled “Workflow Logs”

Monitor AI Flow execution in real time.

Examples include:

  • Trigger execution
  • Step completion
  • Conditional branches
  • Approval checkpoints
  • Workflow outcomes

RAG Logs

Section titled “RAG Logs”

Understand how enterprise knowledge is being used.

Examples include:

  • Embedding generation
  • File ingestion
  • Chunk creation
  • Vector storage
  • Retrieval results

Execution Traces

Section titled “Execution Traces”

View complete execution timelines across multiple components.

Example:

User Request
      ↓
Agent
      ↓
Vector Search
      ↓
MCP Tool
      ↓
Approval
      ↓
Workflow Completion

This allows teams to understand the full lifecycle of an AI request.

Token Usage

Section titled “Token Usage”

Track model consumption across your organization.

Examples include:

  • Prompt tokens
  • Completion tokens
  • Total token usage
  • Usage by model
  • Usage by workspace

Cost Analytics

Section titled “Cost Analytics”

Understand the financial impact of AI workloads.

Examples include:

  • Cost per request
  • Cost per agent
  • Cost per model
  • Cost trends over time
  • Workspace-level spending

Debugging

Section titled “Debugging”

Quickly identify and resolve issues.

Examples include:

  • Failed tool executions
  • Retrieval failures
  • Authentication errors
  • Workflow interruptions
  • Approval bottlenecks

Example Execution Timeline

Section titled “Example Execution Timeline”

An employee submits a request:

My VPN access has expired. Can you restore it?

Karyam records:

10:01 User Request Received
10:01 Agent Execution Started
10:01 Vector Search Executed
10:01 VPN Policy Retrieved
10:02 HR Verification Completed
10:02 Approval Request Sent
10:05 Manager Approved
10:05 Ticket Created
10:05 Employee Notified
10:05 Execution Completed

Every action is visible.

Every decision is traceable.

Every workflow is observable.

Built for Production AI

Section titled “Built for Production AI”

Observability is not optional for enterprise AI systems.

Organizations need:

  • Reliability
  • Governance
  • Compliance
  • Cost control
  • Debugging capabilities

Karyam provides the visibility required to operate AI systems at scale.

Enterprise Governance

Section titled “Enterprise Governance”

Observability works alongside Karyam’s governance capabilities.

Together they provide:

  • Audit Logs
  • Role-Based Access Control (RBAC)
  • Human Approvals
  • Execution Policies
  • Compliance reporting

The Karyam Philosophy

Section titled “The Karyam Philosophy”

Traditional AI systems optimize for demos.

Karyam optimizes for production.

Knowledge
    +
Agents
    +
Skills
    +
MCP
    +
Workflows
    +
Approvals
    +
Observability
        ↓
Production AI Systems

Because organizations don’t just need AI that works.

They need AI they can trust.

Next Steps

Section titled “Next Steps”

Explore the different observability capabilities available in Karyam:

  • Agent Logs
  • Workflow Logs
  • RAG Logs
  • Execution Traces
  • Token Usage
  • Cost Analytics
  • Debugging