Agent Governance

When software engineers collaborate with autonomous AI coding agents, the default workflow is almost always chat-and-code: describe a feature in natural language, watch the model propose a code diff, test it, and iterate. For small, single-function scripts, this works reasonably well. But for production-grade distributed systems, microservices, and multi-module architectures, this approach invariably breaks down. Left unconstrained, AI models suffer from Specification Conflation: they blur the line between WHAT & WHY (the architectural contracts, invariant boundaries, and failure modes) and HOW (the file edits, variable names, and task sequencing). The result is predictable: ...

In our earlier architecture deep-dives, we examined how Attention Dilution cripples autonomous coding agents. As context windows swell past tens of thousands of tokens with terminal traces, compiler diagnostics, and speculative file edits, an agent’s attention mechanism degrades. Critical system constraints, architectural decision records (ADRs), and testing invariants drift into the periphery, leading to hallucinations, ignored instructions, and silent error suppression. To solve this in Google Antigravity, we built the Antigravity Attention Guard Plugin—a deterministic runtime firewall that enforces subagent delegation, sandboxed tool execution, and cryptographic ledger tracking. ...