Building With AI Biological Coordination Patterns Applied to Multi-Agent Software Delivery Coordination overhead grows faster than agent count. The brain solves the same problem with specialization, inhibition, periodic synchronization, and convergence, and each translates into a concrete orchestration primitive for multi-agent delivery.
Building With AI Adversarial Reasoning Architecture: A Multi-Track Approach to AI Agent Reliability AI reasoning fails worst when it is confidently wrong. A structurally separate challenger track that attacks the primary reasoning chain catches flawed assumptions a single model misses.
Building With AI Execution Safety Architecture for AI-Generated Outputs: A Layered Approach AI outputs like code need execution safety checks beyond model evaluation. A layered architecture of scope analysis, static code review, and runtime monitoring limits risk when AI generates what runs.
Building With AI Calibrating Verification Depth to Query Stakes in AI Learning Systems Uniform verification either wastes compute on low-stakes queries or under-invests in high-stakes ones. Consequence-driven verification calibrates depth, redundancy, and conservatism to the cost of being wrong.
Building With AI The Limits of String Matching in Knowledge Coverage Metrics String matching for knowledge coverage seems simple, and it fails in production the moment terminology varies: a system that knows Universal Gravitation reports zero coverage on gravity. A layered matching architecture fixes both failure directions.
Building With AI Structuring AI Knowledge Systems Around Principles: Lessons from Building a Learning Engine Search-index retrieval finds similar documents. Reasoning needs the logical structure of a domain: principles, concepts, processes, and functions, ingested in dependency order. Lessons from building the Helix learning engine.
Building With AI Cost Observability in AI-Assisted Development: Understanding What Your Agents Spend AI agent costs scale with context complexity more than output volume, which makes them harder to predict than traditional compute. Per-task instrumentation is what makes the spend legible and optimizable.
Building With AI Structured Recovery Orchestration for AI Agents: Beyond Retry Transient agent failures yield to retry. Structural failures do not. A seven-phase recovery pipeline that classifies failures, routes them to the right response, and converts each recovery into prevention.
Building With AI Unidirectional Quality Gates in AI-Assisted Development: The Ratchet Pattern AI-assisted development can lead to gradual quality regressions. The "ratchet pattern" enforces monotonically non-decreasing quality metrics, blocking merges that lower any measured value below a recorded floor.
Building With AI Direct API Integration for AI Agents: Lessons from Removing the CLI Layer Spawning a vendor CLI as a subprocess is the fast path to AI agent integration, and it breaks down at scale. Direct API calls behind a thin adapter make production orchestration more reliable and far easier to debug.
Building With AI Architecture Drift at AI Speed: When Your Agents Don''t Respect Boundaries AI coding agents can quickly erode your architecture's integrity when generating code at speed, creating a compounding problem of technical debt. Boundaries need explicit enforcement to prevent parallel agents from turning velocity into chaos.
Building With AI Building an Evaluation Harness for Prompt Engineering: Moving Beyond Intuition The dominant prompt workflow is iterate, eyeball, deploy. An evaluation harness with a golden dataset, scoring rubric, automated runner, and regression gate turns prompt quality into something you can measure and defend.
Building With AI The Review Bottleneck in AI-Assisted Delivery AI coding tools sped up generation, but code review hasn't kept pace. This asymmetry is slowing down delivery and creating a hidden bottleneck for teams.
Building With AI Decoupling AI Agent Logic from Model Providers: The Dispatcher Pattern Like databases, AI model providers change frequently. The Dispatcher pattern decouples your agent logic from specific models, avoiding costly migrations as the AI landscape evolves.
AI The Scaffolding Just Got a Learning Rate New research treats the agent harness as a trainable object, with learning rates and validation gates. A few accepted edits to a skill file moved frozen models further than most teams expect a model upgrade to.
Building With AI The Single-Agent Ceiling: Why One AI Can''t Hold Your Whole Delivery Lifecycle Single-agent AI promises end-to-end code generation, but most teams hit a productivity ceiling within months. Relying on one AI to handle your entire delivery lifecycle creates code that degrades the system and increases downstream costs.
Building With AI The Scaffolding Is the Product: Why AI in Software Development Is a Systems Integration Problem Model accuracy degrades predictably as task complexity grows. The durable advantage in AI-assisted software development is the scaffolding around the model: context, tests, pipelines, and feedback loops.
Building With AI Code Without Requirements Is Just Expensive Guessing AI made code cheap. It did not make rework cheap. Requirements are the constraints that keep agents pointed at the right problem, and the teams that write them down before the first prompt are the ones that avoid the 60-day velocity wall.
Building With AI Using Evaluation Data to Match Model Capability to Task Requirements Benchmark performance and task-specific performance diverge. Running your golden dataset against candidate models, segment by segment, produces routing decisions grounded in evidence instead of tier prestige.