Agent infra: harnesses, workspaces, security, cost, and vectors

Building an Advanced Agentic Harness explains composing small, testable primitives into a production-ready agent harness that plans, executes, verifies, and budgets multi-step LLM workflows. Outcome engineers should treat harnesses as first-class infrastructure — this is a compact checklist for turning brittle scripts into auditable, testable execution pipelines (Principle 09, 13).

Prompt injection isn’t the bug, AI agent frameworks are reports Check Point’s discovery of eleven vulnerabilities across popular agent frameworks, showing the plumbing — not models — is the real perimeter failure. If you run agents in production, this reframes your threat model: harden the framework, not just the prompt or model controls (Principle 10, 14).

Sapiom raises $35M to slash AI agent bills; Anthropic backs the startup announces a model-routing service that directs agent requests to cheaper models to cut costs. Cost-aware routing changes architecture trade-offs for long-running agent workflows — plan routing, fallbacks, and telemetry into your orchestration layer to keep outcomes affordable (Principle 09, 12).

Cloudflare open-sources Cloudflare OS, a browser AI agentic workspace for enterprises open-sources a browser workspace that lets employees build micro-apps and agent-powered automations. Ship an internal, legible workspace like this to scale agent use safely and cheaply across teams — it’s the platform layer that converts prototypes into repeatable outcomes (Principle 03, 09).

AWS updates DynamoDB with native vector search to ease AI application development adds built-in vector search to the operational database so you can store embeddings and transactional data in one place. This collapses the two-database pattern, reduces staleness and infra complexity, and speeds delivery of retrieval-dependent agent features in production (Principle 04, 06).