Releases·Tool Release
Manifesto: letting a UI and an agent use the same app-owned actions
The creator of Manifesto, a free MIT-licensed project, announced its release. It lets a UI and an AI agent change application state through the same typed actions and validation rules, so approvals, policy and external effects are owned by the app rather than bolted on. The code is on GitHub with docs and a runnable React Todo plus scripted-agent example.
1 engagement1 sourceFri, Oct 2, 2026, 11:18 AM
AI Overview
- Creator released free MIT-licensed Manifesto: domain transitions defined in MEL, executed via one SDK runtime shared by UI, backend routes, and agents
- Core computes transitions and Host carries out declared effects; optional Lineage and Governance extensions add history, policy, and approvals
- Public example pairs a React Todo UI with a scripted agent sharing one runtime, updating the same state from either path
Related
Products·launched
Rebuno - open-source execution runtime for operating AI agents in production
Rebuno is an open-source runtime that manages execution state and guardrails for AI agents in production, regardless of the framework they were built with. Agents submit tool and model calls as steps before executing them, and Rebuno evaluates each step against per-agent YAML policies, allowing, denying, or holding calls for human approval. It solves the problem of safely operating agents built with different frameworks in production environments.
Rebuno is an open-source runtime that manages execution state and guardrails for AI agents in production, regardless of the framework they were built with. Agents submit tool and model calls as steps before executing them, and Rebuno evaluates each step against per-agent YAML policies, allowing, denying, or holding calls for human approval. It solves the problem of safely operating agents built with different frameworks in production environments.
3 engagement1 sourcehackernewsMon, Sep 14, 2026, 04:29 PM
Developments·Paper
Beyond Approved Actions: Runtime Validation of Persistent Outcomes in Agent Workflows
An arXiv paper introduces EffectMatch, a runtime that collects persistent changes produced by LLM agent actions within a controlled execution boundary and compares them against what the application approved before allowing commit and dependent execution. On 206 public business tasks it preserved all clean executions and blocked all tested incorrect commits, addressing cases where an approved action succeeds but leaves unapproved side effects that propagate downstream.
An arXiv paper introduces EffectMatch, a runtime that collects persistent changes produced by LLM agent actions within a controlled execution boundary and compares them against what the application approved before allowing commit and dependent execution. On 206 public business tasks it preserved all clean executions and blocked all tested incorrect commits, addressing cases where an approved action succeeds but leaves unapproved side effects that propagate downstream.
0 engagement1 sourcearxivFri, Sep 25, 2026, 02:11 PM
Products·launched
AgentZ: open-source agent workflow platform with built-in governance
AgentZ is an open-source workflow platform for running LLM agents, maintained by AccuKnox. It adds a governance layer on top of agent frameworks: controlling who can run workflows, what resources they can reach, and how risk policies apply when teams reuse shared agents. It targets teams deploying agents to production who need permissions, sandboxes, and policy enforcement beyond what LangChain or AutoGen provide.
AgentZ is an open-source workflow platform for running LLM agents, maintained by AccuKnox. It adds a governance layer on top of agent frameworks: controlling who can run workflows, what resources they can reach, and how risk policies apply when teams reuse shared agents. It targets teams deploying agents to production who need permissions, sandboxes, and policy enforcement beyond what LangChain or AutoGen provide.
9 engagement1 sourceredditSat, Sep 5, 2026, 04:03 PM
Products·building
Vendo - open-source layer for AI agents to render real UI components, not markdown
Vendo is an open-source layer that lets LLM agents render real UI (charts, forms, settings panels) inside a product by picking from a fixed set of pre-declared components. It solves the problem of agents returning only markdown text and requiring custom glue per UI case. Aimed at developers building agent-driven products.
Vendo is an open-source layer that lets LLM agents render real UI (charts, forms, settings panels) inside a product by picking from a fixed set of pre-declared components. It solves the problem of agents returning only markdown text and requiring custom glue per UI case. Aimed at developers building agent-driven products.
2 engagement1 sourceredditThu, Sep 3, 2026, 09:17 PM
Products·launched
Open-source execution record for AI agents (Intent vs. Reality)
Sentience Governor is an open-source governance runtime that records AI agents' tool activity, separating declared intent, recorded execution, and retrospective explanation. It helps developers audit and understand what agents actually did versus what they claim, addressing trust and transparency in agentic systems.
Sentience Governor is an open-source governance runtime that records AI agents' tool activity, separating declared intent, recorded execution, and retrospective explanation. It helps developers audit and understand what agents actually did versus what they claim, addressing trust and transparency in agentic systems.
10 engagement1 sourceredditMon, Aug 10, 2026, 09:18 PM