playbook
4 stories filed under playbook on Beyond Market Intelligence. The newest of them: “Anthropic's S-1 shows how platform cross-selling deepens enterprise loyalty”, “Treat AI agents as infrastructure to stop hallucinations at scale”, and “300K lines refactored for $4,000: what a C codebase taught AI agents”. Anthropic's S-1 reveals something telling about enterprise loyalty: more than half of its $100,000-plus customers now use both the Claude Platform and a first-party app. Hallucinations in AI agents aren't a bug to fix in each app; they're a symptom of treating the LLM stack like an application. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every playbook story on Beyond Market Intelligence, newest first.
Anthropic's S-1 shows how platform cross-selling deepens enterprise loyalty
Anthropic's S-1 reveals something telling about enterprise loyalty: more than half of its $100,000-plus customers now use both the Claude Platform and a first-party app. That's a cross-selling playbook straight out of Amazon's early days, and it's working. When you own two touchpoints, you're not just a vendor, you're infrastructure. For users feeling the weight of outdated tools, this is a glimpse of how sticky smart product ecosystems can become. (See our coverage of how Anthropic's early backers hold billions before the IPO bell rings.)
Treat AI agents as infrastructure to stop hallucinations at scale
Hallucinations in AI agents aren't a bug to fix in each app; they're a symptom of treating the LLM stack like an application. Aditya Mulik's experience with an inventory recommendation system led him to a smarter approach: treat the LLM as shared infrastructure. By building a common platform with prompt versioning, schema enforcement, and per-request cost attribution, his team stopped hallucinations at scale. It's a practical, infrastructure-first mindset. For more on reducing AI costs, see our piece on smarter data structuring with SerpApi.
300K lines refactored for $4,000: what a C codebase taught AI agents
Three hundred thousand lines of C, refactored in three weeks for $4,000 in tokens. CodeScene's case study is a practical stress test for AI agents, not a headline. The playbook of codebase-specific recipes the agents built is the real prize, it suggests these tools learn context, not just syntax. Practitioners are right to question the scope and the harness's role. That skepticism is healthy.
Validating LLMs: What Banking Model Rules Teach Us About AI Testing
Banks have spent decades refining model validation, but large language models don't fit neatly into those established frameworks. This piece examines what actually breaks when the rules shift from statistical models to generative systems, and more importantly, what survives the transition. It's a practical look at testing output quality where traditional assumptions no longer hold. For anyone wrestling with AI governance, this offers a grounded starting point.