Agent Infrastructure: Harnesses, Sandboxes, Routing, and Confidence

Towards a Harness That Can Do Anything proposes a lightweight, Unix-inspired harness to minimize LLM context, enable deterministic workflows, and ensure auditability and runtime self-healing. Outcome engineers get a concrete pattern for reducing brittle context and making agent runs reproducible and auditable — treat the harness as a minimal control plane (Principles 06 & 14).

What building Shippy taught us about building agents demonstrates building trustworthy maritime agents through versioned skills, deterministic tools, sandboxed hosting, and live-data validation. Use it as a pragmatic cookbook: version skills, pin deterministic tools, and validate live-data to turn agent prototypes into verifiable production artifacts (Principles 07 & 16).

Perplexity launches SPACE sandbox to make its AI agents secure and powerful launches SPACE, a secure sandbox that lets Computer agents run at full capability while isolating risk. Sandboxing becomes the practical execution boundary for high-risk agent actions — run integrations and potentially dangerous capabilities inside isolated environments (Principles 07 & 14).

Model Routing Is Simple. Until It Isn’t. argues model routing requires multi-dimensional optimization across caching, infrastructure, and governance, and that naive selection heuristics fail in real systems. Outcome engineers must design routing as a system problem — balancing latency, cost, cache behavior, and policy controls rather than treating it as a single-model choice (Principles 09 & 11).

Uncertainty Quantification for LLM Function-Calling presents methods to quantify function-call confidence so systems can gate execution and prevent irreversible errors. Implement confidence-based gating to turn model outputs into auditable control signals for human oversight and automated safety gates in production agent workflows (Principles 14 & 16).