A new Agentic Maturity Model (AMM) argues that enterprise AI maturity is not measured by adoption metrics but by the mechanism that catches errors before deployment. The model defines five levels of t…
Enterprise AI maturity models that measure seats, prompts, and executive sentiment fail to assess whether AI work is correct, according to a new framework called the Agentic Maturity Model. The model …
A framework for AI maturity defines five levels of organizational trust in AI, from prohibition to operationalization, arguing that true maturity depends not on tooling but on who or what verifies AI …
The Diagonal Law states that AI capability must be matched by verification to avoid risk or waste, defining a grid where off-diagonal states cause enterprise AI failures. The article introduces two la…
RAG (retrieval-augmented generation) is only a low-level knowledge maturity stage, not a final destination, according to a new framework that distinguishes runtime knowledge from compiled skills. The …
Enterprise AI programs often mistake acceptance rate for a measure of review integrity, but it only measures approvals, not attention, creating a dangerous gap in observability. Observability is a pre…
A new economic model for AI adoption, the 'Level 4' framework, argues that organizations can simultaneously be top spenders and bottom performers by measuring the wrong metrics. The model proposes shi…
Enterprises adopting multi-agent workflows are failing because they apply the human-focused Software Development Lifecycle (SDLC) to non-human AI agents, which have different failure modes. The propos…
Enterprises deploying multi-agent workflows often fail because they apply the human-centric Software Development Lifecycle (SDLC) to non-human builders, whose failure modes differ. The proposed Agenti…
Enterprise teams replicating the software development lifecycle with AI agents are importing defenses against human flaws that models don't have while missing model-specific failure modes like halluci…
Enterprise teams are replicating the software development lifecycle (SDLC) by assigning AI agents to human roles like product manager and code reviewer, but this approach imports defenses against huma…
A new agentic lifecycle introduces eight phases with deterministic gates between each pair, limiting human intervention to exactly two mandatory moments: approving the spec and a final review. The lif…
A new agentic parallel development framework introduces three dials—cost, wall clock, and accuracy—to manage multi-agent setups without merge hell. The system uses deterministic control flow with pool…
The ADLC toolkit, an eighteen-tool suite enforcing a deterministic machine-checked development lifecycle, was built by the lifecycle itself and then dogfooded by planting bugs in its own diffs to cali…
Developer Voodoo Tiki extracted OpenAI's adversarial review from the Codex CLI runtime and released it as a standalone tool, adversarial-review, enabling any model or CI pipeline to catch bugs by havi…
Vercel's Head of Forward Deployed Engineering introduces skill mining, a method to extract latent knowledge from codebases into reusable agent skills. The approach uses a seven-phase process to survey…