Your agent returned the wrong answer. Can you explain why?
How to design traces, spans, metadata, sampling, and quality signals that make production AI failures explainable without logging everything.
Insights
Deep dives into AI Agents, Agentic RAG, and the future of autonomous software development.
Agent governance starts with the blast radius
A technical design for agent permissions, tool boundaries, prompt injection defenses, PII controls, approvals, and audit evidence.
The incident runbook for production AI
A practical incident process for detecting, containing, diagnosing, correcting, and learning from failures in agents and RAG systems.
From AI PoC to production: where transformation begins
A working AI demo proves that an idea is possible. Production, adoption, and workflow redesign determine whether it changes the business.
Select the model last: an evaluation-first AI delivery plan
An eight-week delivery plan that defines quality, data, traces, tool boundaries, and operational ownership before selecting the production model.
From AI PoC to production: the engineering system around the model
A technical playbook for turning an AI prototype into a system you can evaluate, trace, operate, secure, and scale.
Multi-agent systems: choose the coordination cost before the pattern
A production guide to deterministic graphs, routers, supervisors, event choreography, handoffs, state, checkpoints, and human approval.
Evaluation is the specification: how to test AI systems before users do
A production evaluation architecture for AI agents, including datasets, deterministic checks, LLM judges, trajectory tests, and release gates.
Production RAG is a data lifecycle, not a vector database
How to engineer ingestion, freshness, lineage, permissions, deletion, retrieval evaluation, and telemetry for dependable production RAG.
Prompts and models are production dependencies
How to version, evaluate, canary, roll back, and audit prompt and model changes without turning production into the test environment.
How to Actually Map a Workflow Before You Automate It
Before automating a workflow, map the real work - including exceptions, undocumented decisions, handoffs, and shadow processes that never appear in the official version.
How to Think About AI Transformation
AI transformation starts with redesigning work around the right mix of people, conventional software, and AI - not with building an agent for its own sake.
AI coding agents expose the real bottleneck: unfinished decisions
When an agent can implement a ticket in an hour, every unanswered product and technical decision becomes visible in the diff. The teams that ship faster learn to make those decisions explicit before they ask for code.
How to scope an AI agent proof of concept
A proof of concept should answer one operational question, define its boundaries, and leave a clear decision for what happens next.
LangSmith Agent Builder + MCP: What Worked, What Broke
A practical breakdown of building production agents with LangSmith Agent Builder and MCP tools-where the promise met reality, and how to actually get MCP tools working.
9 AI Agent Use Cases Reshaping E-commerce in 2026
How autonomous AI agents are transforming e-commerce through personalization, support automation, and agent-to-agent commerce-moving beyond chatbots to active participants in the customer journey.
Enterprise AI in 2026: From Hype to Hard Value
Why 2026 may finally be the year enterprise AI moves from experimentation to measurable business value, and why most vendors will not survive the transition.