The Anatomy of AI Agents: How Machines Sense, Think, and Act
From virtual assistants to autonomous vehicles, AI agents are everywhere. Learn the three-stage Sense-Think-Act architecture.
Speed and safety are not opposites. They are partners when governance is built in.
42 articles
From virtual assistants to autonomous vehicles, AI agents are everywhere. Learn the three-stage Sense-Think-Act architecture.
Why two-thirds of organizations are stuck in pilot purgatory, and how to join the 8.6% that reach production with AI agents.
A PM platform, a security team, and an infra provider independently built governed AI agents. They converged on four identical patterns.
Stripe and Paradigm launched MPP with Visa, Mastercard, and both AI labs. The protocol is live. The governance is not.
Vint Cerf says trust is infrastructure. Lean 4 says types are proof. A Haskell expert says specs become code. All three are right.
Three companies shipped agent containment in one week. The pattern is identical: YAML policies, egress proxies, credential isolation.
GitLab cut SOC controls by 58% with a custom framework. AI can now rewrite GPL code in days. Both stories reveal how governance actually works.
Three independent frameworks converge on the same conclusion: agent specs are not documentation. They are auditable, enforceable governance infrastructure.
The Agent Skills standard solves what monolithic agents never could: modular, auditable, version-controlled AI capabilities.
Three practitioners independently rediscovered the same truth: AI agents need engineering discipline, not new frameworks.
Reliable AI agents come from environmental constraints, not better prompts. Three independent sources converge on the same architectural principle.
Three companies running AI agents at scale converged on the same principle: maximum autonomy inside structural constraints.
Most AI agents forget everything between sessions. Learn how runtime learning transforms agents from tools into teammates.
A simple filesystem outperforms sophisticated memory solutions. Discover what benchmarks reveal about memory architectures for AI agents.
How to use AI that acts and free up time for what matters. Use cases and tools for PMs without code.
Insights from Uber's Gen AI on-call copilot. RAG vs fine-tuning, Spark pipeline, and the quality secret.
The definitive guide for specifications that work. 5 principles tested by Google and GitHub engineers.
The difference between AI that responds and AI that acts. How agentic systems transform expectations and deliver 4x productivity.
McKinsey went from measuring AI wrong to calling it a design problem to using the word governable. The pattern reveals more than any single article.
McKinsey says AI's scaling problem is a design problem. They're half right. Design is the interface layer of governance.
Uber and Stripe built governance into infrastructure. Microsoft hired a quality head. The pattern reveals what most companies are missing.
OpenAI published four personality profiles for AI agents. They missed the point. Personality is fleet-wide behavioral governance, not prompt cosmetics.
Executives self-report 16-45% AI gains. Controlled trials show 19% slowdown. The perception mismatch is not noise. It is missing infrastructure.
Hyperscalers give away agent SDKs to sell runtime. The real contest is governance: security, evaluation, context control. Bet on that layer.
Spec-driven development compresses PM cycles. It also turns every unclear requirement into a production risk nobody reviews.
When consulting firms deploy your AI agents, they also define your governance. Enterprises need to decide who owns the rules.
LLMs don't just write tactical code. They turn entire organizations into tactical tornadoes. The fix isn't better code review.
Apple mapped 55 UX features for AI agents. The finding most teams miss: governance that users cannot see is governance that does not work.
Cursor, Docker, Zenity, and Entire shipped four distinct containment layers in one week. The shift from approval fatigue to trust boundaries is here.
Bounded autonomy is the right design target for AI agents. Platform engineering is the governance layer most organizations already have.
Anthropic published a guide to fix generic AI designs. The real lesson: output quality requires the same governance as safety and compliance.
Every concern about AI code generation maps to a governance failure, not a technology deficiency. The question was never whether to use AI.
Product teams encoding brand rules into CLAUDE.md are doing governance-as-code. Content ops proves the pattern.
AI is reshaping software careers. The advice to 'learn business' misses the point. The premium is in governance-aware architecture.
AI compresses feasibility, viability, and usability risk. Desirability becomes the only differentiator. What changes for product teams.
Why AI agents fail in production and how orchestration fixes it. Temporal, Conductor, and LangGraph compared.
How leading companies integrate AI into design processes with templates, instruction files, plugins, and internal chatbots.
Why conservative design beats clever automation. Lessons from nuclear engineering for building AI systems that fail gracefully.
Analysis of Anthropic's 23,000-word framework and how to apply its principles to corporate AI governance.
Google UCP enables AI agents to complete purchases conversationally. See how retailers integrate and what it means for e-commerce.
With salaries reaching $4,000/month, AI agent specialists are among Brazil's most sought-after professionals. But what does this mean for your company?
After a year of agentic AI projects, clear patterns emerge. Six fundamental lessons for capturing real value with autonomous agents.
Building and deploying AI with governance baked in from day one.
Start Your Implementation