Technical Architecture
Patterns for building autonomous AI systems that plan, reason, and execute multi-step enterprise workflows. From single-agent copilots to multi-agent orchestration with human-in-the-loop governance.
Everyone is shopping for the best model. But in a production AI agent, the model is roughly 10% of the system. The other 90% is the harness — and that is where reliability, and your competitive advantage, actually live.
The bottleneck is no longer the AI — it is your ability to instruct it. A practitioner's assessment of Windsurf, Cursor, Claude Code, OpenAI Codex, and Google Antigravity, with a focus on the human skills that separate productive teams from frustrated ones.
Tail spend is not a sourcing problem. It is a decisioning problem—thousands of low-value choices that are too numerous for human intervention and too variable for static workflows. That is precisely where agent-based systems fit.
Stop reviewing NDAs from scratch. Architect a Legal Agent that enforces your playbook automatically.
Don't use LLMs for math. Use a hybrid architecture for governed AP automation.
Autonomous sourcing agents, tail spend decisioning, and supplier evaluation — architectural patterns for transforming so...
Contract redlining agents, clause extraction, and compliance automation — technical architectures for legal teams lookin...
Autonomous supplier risk detection, real-time monitoring, and predictive disruption alerts — multi-source intelligence p...
Strategic frameworks for scaling AI from proof-of-concept to production deployments. Covering ROI measurement, governanc...
Designing an agentic AI system? Let's discuss architecture, orchestration, and governance patterns.
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