Blog
Insights on AI agent observability, governance, accountability, and the engineering practices that make autonomous systems trustworthy.
Evaluating AI Agents in Production: Beyond Offline Benchmarks
Offline evals tell you how an agent might perform. Production monitoring tells you how it actually performs. Here is how to bridge the gap with evaluation strategies that work at scale.
Token Economics: Profiling and Reducing LLM Costs in Multi-Agent Systems
A single agent call costs pennies. A multi-step workflow with retries and context assembly can cost dollars. Here is how to see where every token goes and systematically reduce spend.
Human-in-the-Loop Done Right: Designing Review Gates That Scale
Most HITL implementations either gate everything (killing velocity) or gate nothing (risking incidents). Here is how to design review workflows that balance safety with speed.
AI Regulation in 2026: What the EU AI Act Means for Agent Builders
The EU AI Act is now in enforcement. NIST AI RMF is the de facto US standard. A practical guide to what these regulations require and how to map them to engineering controls.
When AI Agents Fail: Post-Incident Analysis for Autonomous Systems
Traditional post-mortems assume a human made a decision. Agent incidents require a new playbook — one that reconstructs reasoning traces, identifies systemic failure modes, and prevents recurrence.
Agentic Protocols Compared: MCP, A2A, ACP, and the Protocol Landscape
MCP connects models to tools. A2A connects agents to agents. ACP standardizes agent communication. Here is how they differ, when to use each, and why observability across all of them matters.
SDK and CLI: How to Instrument and Operate AI Agents with TuringPulse
The SDK instruments your agents with automatic tracing, KPIs, and governance. The CLI lets you explore production data and manage configuration. Here is how they work together.
Provenance Engineering: Making Every AI Decision Reproducible
When a regulator asks why your agent approved a loan or denied a claim, can you reconstruct the exact context, reasoning, and model state that produced that decision? Provenance engineering makes the answer yes.
Drift Detection for AI Agents: Catching Behavioral Shifts Before Users Do
A model update, a data source change, or a subtle prompt regression can shift agent behavior in ways that take weeks to notice. Drift detection catches these shifts in hours.
Safe Agent Deployments: Canary Releases, Shadow Mode, and Progressive Rollouts
Traditional deployment strategies were designed for deterministic software. AI agents require adapted patterns — shadow mode, progressive rollouts with quality gates, and automated rollback triggers.
Instrumentation at Scale: The Universal Plugin Architecture for AI Agents
AI frameworks are multiplying faster than teams can keep up. Here is how a universal plugin architecture lets you instrument LangGraph, CrewAI, DSPy, Haystack, and 12 more frameworks without writing custom integrations.
Change Intelligence: How Fingerprinting and Deploy Tracking Prevent AI Regressions
A 2% prompt tweak can cause a 30% quality drop that takes weeks to notice. Change intelligence connects every deploy, config change, and prompt update to its downstream behavioral impact.
Principles and Patterns of Building Agentic AI Systems
From choosing the right model to orchestrating multi-agent workflows — the foundational ideas and battle-tested patterns shaping how production AI agents are built today.
Observability for AI Agents: Beyond Logs and Metrics
Traditional APM tools were built for deterministic software. AI agents are anything but. Here is how to instrument, trace, and understand autonomous systems that think before they act.
Governance as Code: Codifying Trust in Autonomous AI
What if every governance policy — drift thresholds, review gates, escalation rules — lived in version-controlled code instead of slide decks? Welcome to Governance as Code.
Accountability as Code: Building Provable AI Audit Trails
When an AI agent makes a consequential decision, can you prove why? Accountability as Code turns every agent action into a cryptographically verifiable, tamper-evident record.