Managed Agent Platform vs. Build Your Own: When Each Wins
A decision framework for enterprises choosing between managed agent platforms and custom-built agent infrastructure—covering cost, control, and deployment
THE RECORD BEHIND THE WORK
Operational intelligence, frameworks and evidence—organized as one enduring institutional record.
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A decision framework for enterprises choosing between managed agent platforms and custom-built agent infrastructure—covering cost, control, and deployment
Compare GPT-4o, Claude, and Gemini for production agent deployments. A methodology guide for selecting the right foundation model by task.
Learn how to construct an enterprise knowledge graph that powers reliable AI agents—covering schema design, ingestion pipelines, and ongoing maintenance.
A technical scorecard for evaluating agent framework maturity—covering architecture, tooling, observability, and production readiness before you commit.
Bad data silently degrades AI agents. Learn how data quality acts as a reliability multiplier and what infrastructure decisions prevent cascading failures.
Discover the data architecture foundations every organization must build before deploying production AI agents—pipelines, governance, and more.
Learn how prompt drift across model updates silently breaks AI agents and the detection methods that catch it before production fails.
Poor system prompt design is the leading cause of production AI agent failures. Learn the methodology that makes agents reliable at scale.
Learn how to design AI agent systems for graceful degradation versus hard stops, with frameworks for failure modes, resilience, and safe deployment.
How much does one bad AI agent decision cost? A vertical-by-vertical breakdown of catastrophic failure modes, real exposure, and what prevents them.
A practical methodology for red-teaming AI agent instructions against prompt injection, adversarial inputs, and instruction override attacks.
Learn how to design instruction hierarchy in multi-agent systems so agents operate without conflict, duplication, or directive collision.
How do you test agent prompts at scale before production deployment? A layered methodology covering regression suites, adversarial inputs, semantic scoring
A documented breakdown of AI agent failure modes in production—hallucination propagation, tool misuse, runaway loops, and the firms building to prevent them.
Independent audit frameworks for deployed AI agents explained—what reviewers check, how benchmarking works, and what gaps most audits leave open.
Which SLA standards should govern deployed AI agents? A category-by-category breakdown of what vendors and operators should actually commit to.
How to structure insurance and indemnification clauses in an AI agent contract—liability scope, IP risk, and vendor transfer of risk.
A rigorous look at the KPIs that separate production-grade AI agents from prototypes, with benchmarks from leading deployment firms.
How should procurement teams classify an AI agent: software, service, or employee? A framework covering governance, contracts, and risk across all three.
Which AI agent vendors actually meet enterprise SLA standards? A ranked guide to uptime, recovery, and accountability across the leading providers.
Learn which contract structures enterprises need for AI agent maintenance, support SLAs, and long-term deployment governance.
Learn how enterprises build agent literacy without data scientists — frameworks, org structure, talent strategy, and change management that work.
How enterprises should assign AI agent governance ownership across CISO, CTO, and COO functions — with a tiered accountability framework.
How enterprises structure Agent Operations teams to run production AI agent fleets—roles, responsibilities, and org design that scales.