Buildable agenthood: harnesses, shared brains, identity, verification

OzBrain: a shared brain for knowledge between agents and your team centralizes team and agent knowledge into one writable, connector-based brain so every AI reads and updates the same single source of truth. That single-source approach reduces context drift between humans and agents and gives outcome engineers a practical pattern for shared knowledge graphs and consistent context (Principles 03 & 11).

The Evolution of the Agent Harness maps how harnesses have shifted into human-attention scaffolds that connect context, tools, and guardrails. Treat the harness as your composition and observability layer — where orchestration, human handoffs, and outcome controls live in production systems (Principle 06).

The New MCP Roadmap lays out agentic messaging, unified HTTP transports, and enterprise agent identity to make model-driven apps scalable and secure. Adopting MCP-style context and identity primitives lets teams build interoperable agents and enforce policy boundaries across services — foundational for enterprise outcome engineering (Principles 09 & 10).

Six identity capabilities for securing autonomous AI agents argues for verifiable agent identities, ephemeral credentials, and continuous Zero Trust governance to safely deploy autonomous agents. Implementing these identity primitives treats agents as auditable, revocable identities you can gate and monitor — essential to avoid privilege creep and run agents safely at scale (Principles 10 & 15).

More than just code review recommends targeted verification and human checkpoints instead of line-by-line reviews when validating coding-agent changes. Move validation toward outcome-focused tests, proofs, and checkpoints so agents can iterate quickly without becoming a safety or correctness liability (Principles 16 & 15).