Agent-first software engineering, orchestration, and failure-aware systems.

Why agent reliability in 2025–2026 is often driven more by harness engineering—tool gating, verification, retries, termination rules, and tracing—than by marginal base model upgrades.

Why agent infrastructure benefits from a lean microkernel: an explicit control loop, narrow tool contracts, append-only event state, and replaceable transport for predictable cost and behavior.

In AI-assisted, high-velocity codebases, tests stay necessary but cannot be the primary safety system; survivability comes from observability, constraints, and recovery.

An operator-focused case for separating an agent’s autonomy from its permissions and secrets to limit blast radius under prompt injection and model variability.

An operator-focused guide to earning trust in agentic AI through constraints, instrumentation, and iterative verification loops.

Software engineering is shifting from 'Human-in-the-loop' execution to 'Human-on-the-loop' orientation, where humans manage system dynamics and context in a Joint Cognitive System with autonomous agents.

An operator model for turning intent into deployed software via repeatable pipelines and agentized execution.

Prompting treats agents as step-by-step trainees; cultivation treats them as actors embedded in environments where tools, constraints, and feedback loops drive reliability.

Multi-agent coding assistants have reduced software creation costs enough that individuals can now build personal operating systems—control layers that encode decision rules and execution mechanisms into custom micro-apps, shifting knowledge work from passive memory toward active execution.

Why open source must shift from maximizing contribution volume to enforcing strict curation as coding agents drive the cost of code to zero.

A milestone timeline (2013–2026) showing how AI coding tools evolved from autocomplete into terminal-native agents with tool use, planning, and verification loops.

An exploration of how AI agents shift source code from a human artifact to a compilation target, requiring a move from syntax-based review to intent-based validation.

In an era where regeneration is cheaper than comprehension, replaceability becomes the primary architectural virtue.

A systems design approach to selecting the optimal LLM tier within GitHub Copilot to maximize research throughput and minimize cognitive waste.

Exploring the shift from AI-augmented to AI-native engineering, the context stack, and the systemic verification crisis.

Decoupling permission logic from reasoning loops to build safer, more predictable agentic systems.

A governance-first approach to agent orchestration prioritizing predictability, containment, and recoverability over raw productivity.

A risk-mitigated governance framework for integrating AI agents into production software engineering workflows through a staged autonomy ladder.

How specifications, tests, and agents collapse into a single machine-readable contract that governs autonomous labor.

A practical description of an agent-first workflow where engineering shifts from typing code to designing boundaries.

Why the bottleneck in software engineering is no longer typing code, but context and judgment.