Learn how to monitor AI agents in production by focusing on conversation-level signals, multi-step trajectories, and real user interactions rather than traditional system metrics. The article explains why agent observability differs from standard APM due to infinite input space and non-deterministic LLM behavior, and highlights the need to capture prompt-response pairs, multi-turn context, and tool usage traces. It also outlines how production traces become the foundation for continuous improvement and scalable evaluation, combining automated evals with selective human review to maintain quality at scale.
Failures in agentic systems stem from lack of operational ownership of behavior in production—not model accuracy. This highlights gaps between governance, engineering, and customer perception, emphasizing the need for real-time behavioral oversight in deployed AI systems.
Proposes AgentOps as the next evolution beyond DevOps, MLOps, and LLMOps, outlining a structured 9-phase lifecycle framework for developing, deploying, monitoring, and governing AI agents. Focuses on production readiness, guardrails, rollback strategies, observability, compliance, and operational risk management for autonomous agent systems
AI agents are transforming cloud architecture by shifting cloud architects from hands-on infrastructure management to designing intent-driven, policy-based systems. Autonomous agents now handle provisioning, scaling, anomaly detection, root cause analysis, and automated remediation, moving CloudOps toward AgentOps. Architects increasingly define SLOs, guardrails, compliance policies, and cost constraints while agents execute and optimize infrastructure in real time. The article highlights proactive incident management, automated runbooks, digital twins for simulation, embedded compliance enforcement, and human-in-the-loop governance models as core patterns. Success in this new era requires skills in intent modeling, policy design, agent escalation workflows, and telemetry-driven optimization.
A comprehensive guide to LLM evaluation metrics across foundational models, RAG pipelines, and AI agents. Covers correctness, hallucination, task completion, tool correctness, and LLM-as-a-judge approaches (e.g., G-Eval), with architectural framing and code via DeepEval. Focused on evaluating and operationalizing LLM systems in production.
Autonomous AI agents introduce fundamentally new operational challenges that cannot be addressed by traditional MLOps or LLMOps frameworks. They require workflow-first orchestration, declarative capability management, enhanced observability of reasoning and tool usage, runtime guardrails, human-in-the-loop infrastructure, behavioral simulation testing, state and memory management, and workflow-level cost attribution. Agent operations represents a new operational category distinct from model-centric paradigms.
In the AI/agent era, database sprawl creates operational fragility. Instead of stitching together Elasticsearch, Pinecone, Redis, Kafka, teams should consolidate on Postgres with extensions (pgvector, TimescaleDB, PostGIS). This approach simplifies testing, environment forking, uptime, cost, and operational overhead—key factors for running reliable agentic systems at scale.
This article analyzes operational tradeoffs between MCP tools and skills for agent systems, focusing on setup complexity, execution predictability, latency, scalability, and context management. It frames MCPs as structured, networked execution interfaces and skills as local, behavioral context injection—directly addressing how agents are extended, operated, and managed in practice.
Agent engineering represents a new discipline for turning non-deterministic LLM agents into reliable production systems. This approach emphasizes iterative shipping, production observability, evaluation, runtime infrastructure, memory, and performance measurement—highlighting how teams operationalize agents beyond "it works on my machine."
Agentic AI systems have outpaced architectural thinking required to operate them effectively. These agents differ fundamentally from traditional software: their behavior is not fixed at deployment but continuously shaped by experience, feedback, and context. Traditional DevOps or MLOps principles assume system behavior can be managed through versioning, monitoring, and rollback. This assumption breaks down for Agentic AI systems whose learning trajectories diverge over time, introducing non-determinism that makes system reliability challenging at runtime. CHANGE is a conceptual framework comprising six capabilities for operationalizing Agentic AI systems: Contextualize, Harmonize, Anticipate, Negotiate, Generate, and Evolve. CHANGE provides a foundation for architecting an AgentOps platform to manage the lifecycle of evolving Agentic AI systems.
This project demonstrates converting unstructured documents into a concept-based knowledge graph for Graph Retrieval Augmented Generation (GraphRAG). The process covers chunking, LLM-based concept extraction, relationship inference, and local graph construction using an open-source model, enabling more precise, explainable retrieval than vector-only RAG.
A comprehensive guide to LLM evals, drawn from questions asked in our popular course on AI Evals. Covers everything from basic to advanced topics.
Traditional agent browser tools waste tokens and intelligence by forcing models to click, type, and scroll. Smooth CLI introduces a goal-driven interface where agents focus on intent, not UI mechanics. This approach delivers browser automation that is up to 20× faster, 5× cheaper, and designed for the complexity of modern websites.
RAG systems experience failures caused by retrieval poisoning. This analysis evaluates six RAG evaluation frameworks on their ability to detect deceptive negatives, focusing on relevance scoring, ranking metrics, adversarial safety, and how evaluation tooling and prompt design affect agent reliability.
AI agents that make decisions over structured data rely heavily on tabular learning models — but model choice has direct implications for agent reliability, routing, and operational behavior. In this benchmark, we evaluate 7 widely used tabular model families across 19 real-world datasets (~260k rows, 250+ features) to understand which models agents should invoke under different data regimes. Rather than focusing solely on average rank, we analyze win rates to capture dominance — a critical signal when agents must choose models dynamically at runtime. The results reveal that: Foundation models are most effective for agents operating with limited data XGBoost is the most reliable choice for large, numeric-heavy workloads Hybrid datasets at scale remain operationally ambiguous, with multiple viable model choices These findings highlight a core Agent Ops challenge: model selection and routing inside agents is a runtime decision, not a one-time architecture choice. As agents increasingly combine LLM reasoning with structured prediction, understanding the operational strengths and failure modes of tabular models becomes essential for building robust, cost-aware agent systems.
Learn how to build LLM-ready knowledge graphs using FalkorDB to ground AI responses via GraphRAG. This guide covers graph databases, knowledge graph construction, ingestion, deployment, and framework integrations. The focus is on reliable retrieval of private, up-to-date organizational knowledge for GenAI systems.
Research report detailing security vulnerabilities in production agents. Focuses on identity management, unauthorized database access, and the governance gap in 'Shadow AI' agent deployments.
Practical guide on structural governance for IT automation. Discusses the Model Context Protocol (MCP) as a control layer for agent-to-system interactions and hard execution constraints.
Technical analysis of the stateful orchestration required for agents. Discusses sub-millisecond state access, memory architecture (short/long-term), and sub-millisecond vector retrieval for RAG.