Alternatives
Products that do what Greene Comply does
Control layer for agent spending
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Bookkeeping assistant that runs receipt workflows end-to-end
Jun 2026 · receiptor.ai
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Deep Work Plan▲114Models matter. Context matters more. Give your agent a plan.
Jun 2026 · deepworkplan.com
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Open-source security gateway & static scanner for AI agents. Enforce role-based access control (RBAC), human-in-the-loop approvals, segregation of duties, and cryptographically signed, offline-verifiable audit logs. - makerchecker/MakerChecker
Jul 2026 · github.com
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Much of my work right now involves complex, long-running, multi-agentic teams of agents. I kept running into the same problem: “How do I keep these guys in line?” Rules weren’t cutting it, and we needed a scalable, agentic-native STANDARD I could count on. There wasn’t one. So I built one. Here are two open-source protocols that extend A2A, granting AI agents behavioral contracts and runtime integrity monitoring: - Agent Alignment Protocol (AAP): What an agent can do / has done. - Agent Integrity Protocol (AIP): What an agent is thinking about doing / is allowed to do. The problem:…
Feb 2026 · mnemom.ai
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Govern AI agents with approval gates and audit trails
Jul 2026 · bosai.syncguard.fr
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We were both genuinely impressed by Claude Code after it helped each of us fix nasty CI problems overnight. Doing those fixes manually would have taken days. After that experience, we each found ourselves struggling through Ctrl+Tab through multiple Claude Code windows in our terminals. While we enjoyed having agents working for us in parallel, context switching and cycling through each terminal tab was a real pain. So we thought: Can we design a TUI dashboard that manages a large swarm of agents in one place? Even better, can agents manage agents hierarchically, like how companies work?…
May 2026 · omar.tech
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I have spent a long time working in an XP/TDD style, so when AI coding tools became useful enough for real work, I adopted them quickly. The first bottleneck I hit was not code generation, it was verification: AI could write code and tests quickly, but I was still the person reviewing implementations, clicking through flows, checking logs, inspecting database state, and deciding whether the result was actually correct. That pushed me to move validation further left. Before implementation, AI had to produce test plans. After implementation, it had to execute those plans too: drive the…
Mar 2026
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