Services/AI, AIOps & Agents

AI, AIOps & Agents

Deploy policy-aware agent orchestration from MLOps to AgentOps with guardrails, continuous evaluation, and outcome tracking at scale.

Assess Your AI MaturityBook Consultation

From MLOps to AgentOps

AI operations are evolving rapidly. What worked for training and deploying models doesn't work for orchestrating multi-agent systems with tool calling, memory, and decision-making.

1

MLOps

Model training, deployment, monitoring
Tools: MLflow, Weights & Biases, Seldon
2

LLMOps

Prompt management, fine-tuning, cost tracking
Tools: Langfuse, PromptLayer, Helicone
3

AgentOps

Multi-step workflows, guardrails, orchestration
Tools: LangGraph, AutoGen, CrewAI

Core Capabilities

AgentOps Platform

Agent Orchestration

Multi-agent systems with coordination, context sharing, and handoff protocols for complex workflows.

  • ✓ReAct/Chain-of-Thought prompting
  • ✓Tool-calling with function schemas
  • ✓Multi-agent coordination patterns
Policy Guardrails

Embedded controls that ensure agents operate within defined boundaries: content filters, API rate limits, approval gates.

  • ✓PII detection and redaction
  • ✓Output quality scoring
  • ✓Human-in-the-loop triggers
Continuous Evaluation

Automated testing of agent behavior with adversarial inputs, regression suites, and production monitoring.

  • ✓Automated eval harnesses
  • ✓Production trace analysis
  • ✓Drift detection
Outcome Tracking

Measure agent impact on business outcomes: resolution time, user satisfaction, automation rate.

  • ✓Task completion metrics
  • ✓Quality scores
  • ✓Business impact attribution

Essential Guardrail Patterns

Agents need guardrails to operate safely in production. Here are the four essential patterns.

Content Filtering
Use: Block toxic, biased, or inappropriate outputs
Implementation: Llama Guard, Azure Content Safety
PII Detection
Use: Redact sensitive information before logging or storage
Implementation: Presidio, AWS Comprehend
Output Validation
Use: Ensure agent outputs match expected schema
Implementation: Pydantic, Guardrails AI
Rate Limiting
Use: Prevent runaway API costs from agent loops
Implementation: Circuit breakers, token budgets

AgentOps Technology Stack

Build your agent platform in layers, from orchestration to observability.

Orchestration
LangGraph, LlamaIndex, AutoGen
Multi-agent workflows, tool calling
Guardrails
Guardrails AI, NeMo, LlamaGuard
Content filtering, PII detection, output validation
Evaluation
Braintrust, Langfuse, PromptLayer
Automated evals, trace analysis, regression testing
Observability
LangSmith, Arize, Weights & Biases
Production monitoring, latency tracking, cost analysis
Infrastructure
Modal, BentoML, Ray Serve
Serverless inference, autoscaling

Maturity Model

Four Levels of AI Agent Maturity

L1
Chatbots

Single-turn Q&A with RAG, no orchestration or guardrails

  • •OpenAI API calls
  • •Vector search
  • •No memory
L2
Tool-Calling Agents

Agents can call functions, but limited to single workflows

  • •Function calling
  • •Basic memory
  • •Single agent
L3
Multi-Agent Systems

Coordinated agents with handoffs, parallel execution, guardrails

  • •Agent orchestration
  • •Policy enforcement
  • •Automated evals
L4
Autonomous Operations

Agents operate in production with continuous learning and outcome tracking

  • •Self-healing
  • •Drift detection
  • •Business outcome tracking

Real-World Use Cases

Customer Support Automation

Challenge: 10K+ support tickets/month, 48hr average resolution time
Approach: Multi-agent system: triage → research → resolution with human escalation
Results:
  • ✓60% automation rate
  • ✓2hr average resolution time
  • ✓CSAT +18 points

Sales Research Agent

Challenge: SDRs spending 70% of time on account research instead of selling
Approach: Research agent that gathers company data, builds ICP fit scores, drafts outreach
Results:
  • ✓3x increase in outreach volume
  • ✓2x improvement in response rate
  • ✓SDR productivity +45%

Ready to Deploy Production Agents?

Take our AI readiness assessment to understand your maturity level and get a personalized AgentOps roadmap.

Take AssessmentTalk to an Expert