Field notes from forward-deployed engineers.
Practical writing on agentic AI, evals, observability, AI-pair workflows, and what it actually takes to ship production AI 100× faster. 20 posts and counting.
AI-native delivery: how 100× velocity actually works in production
How forward-deployed pods compress engineering cycles by orders of magnitude - the systems, evals, and rituals that separate teams shipping AI features daily from teams stuck in 9-month roadmaps.
From demo to production: the agentic AI engineering checklist
78% of agent demos fail to reach production. Here's the engineering checklist that separates the 22% that survive - orchestration, evals, telemetry, guardrails, cost controls, and the boring stuff nobody puts in keynote slides.
Evals as the product spec: a different way to ship AI features
Stop writing acceptance criteria - write evals. A practical guide to designing eval suites that pull double duty as your product spec, your CI gate, and your trust signal for production.
Multi-agent orchestration patterns for the enterprise
Five battle-tested patterns for composing planner, tool, and worker agents at enterprise scale - without losing context, leaking budget, or shipping non-deterministic regulated workflows.
From RAG demo to RAG that survives production
Most RAG demos work. Most production RAG doesn't. The retrieval, ranking, grounding, and update-pipeline patterns that separate impressive demos from systems your customers can rely on.
AI Rescue: hardening internal copilots without throwing them away
Your team shipped an internal copilot. Security flagged it, cost ballooned, accuracy slipped. The 4-week rescue playbook for production-hardening what's already live - without rewrites.
Forward-deployed engineering vs traditional consulting: a delivery comparison
Why embedded engineering pods ship 4.6× faster than equivalent staff augmentation - the spec-translation tax, operator-feedback loop, and what changes when engineers walk the floor.
AI safety in regulated industries: what auditors actually ask
Regulators don't ask if your model is good - they ask if you can prove it. Audit-ready engineering for HIPAA, SOX, NERC CIP, and EU AI Act compliance in production agentic systems.
The economics of forward-deployed AI: a CFO-grade breakdown
Why a 4-pod 8-week engagement ships at 30% of an in-house build - the cycle-time math, the rework tax, and the lifetime cost story as AI features mature.
Embedded copilots: AI surfaces that live inside your product
Why drop-in SDKs underdeliver and what changes when copilots live in your codebase, design system, and data model - activation, retention, and trust patterns from 12 SaaS rollouts.
AI observability: what to measure when agents go to production
What to instrument, what to alarm on, and how to keep the AI dashboard from becoming a separate observability tool nobody learns. Telemetry patterns for production agents.
Choosing model providers in 2026: Anthropic, OpenAI, open-weight, or all of the above?
Why provider choice ranks below evals on the priority stack - and how a gateway lets you A/B providers per use-case based on pass-rate, latency, and cost rather than vendor relationship.
Mobile-first AI: copilots on iOS and Android without web shortcuts
The engineering shape of a great mobile copilot differs from web - streaming budgets, offline fallbacks, on-device inference, design-system parity. The production playbook for native AI.
HIPAA-grade agents: a working playbook for healthcare AI
What shipping an LLM-powered agent into a healthcare workflow actually takes - BAA-covered providers, PHI redaction, audit trails compliance accepts, and clinical-safety evals.
Agent guardrails: prompt injection, jailbreaks, and exfiltration in production
What stops adversarial inputs in production agentic systems beyond "better prompts" - layered defenses, red-team evidence, and gateway-level controls that survive real adversaries.
Tool design for agents that survive ambiguity
Tool contracts are where most agent projects die in production. Strongly-typed schemas, idempotency keys, retry semantics, and failure-mode mapping turn fragile tool integrations into ones that don't break under adversarial inputs or upstream drift.
Why most AI POCs die before production - and how to fix the diagnosis
70–80% of enterprise AI POCs never reach production. The pattern is consistent across hundreds of post-mortems - scope, evals, ownership, budget. Here's the diagnosis and the response.
Lightning Pods: a 4-week shape for shipping AI features
How a 4–6 person senior pod ships a production AI feature in 4 weeks - week-by-week deliverables, where the velocity comes from, and why this shape beats longer engagements at small scopes.
Cost models for production agentic AI - what to budget, what to instrument
Per-agent cost isn't "$X per token" - it's a stack of model, retrieval, tool calls, storage, ops. The budgeting framework and the telemetry that catches cost surprises before finance does.
From siloed automations to agentic platforms: an architectural shift
A fleet of automations isn't an agentic platform. Four shared layers that change everything - runtime, evals, identity, audit - and how the Nth agent gets cheaper, not more expensive.
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