Komina Agent Harness is an OpenClaw-native deterministic control layer for autonomous agent tool calls. It grew from real agent workflows: BayDar, a scheduled, stateful agent that searches and evaluates Bay Area events and, under standing delegated rules, can automatically register Stephen for events aligned with his work, discovered the HackerSquad workshop that led to this prototype. In the AI age, automation should help people participate more fully in the world, not remove the human element from it. Stephen built BayDar to find the rooms where he could meet people who shared his passions and develop meaningful relationships. But those events are created by real people and involve identity, schedules, commitments, and personal information. When an agent can make commitments for a person, delegated authority must be explicit and auditable. The same authority problem appears in TestBot's self-directed learning loops, an experiment in memory, autonomy, and machine consciousness, and Komina's public-source cold-case research. These agents may propose valuable actions, but they should not judge their own permission to act. The principle is simple: AI proposes. Code decides. Komina validates the actual tool name and arguments against a server-side, runtime-frozen policy manifest. Safe reads can be allowed automatically. Mutations require a persisted human-approval artifact and a registered mock undo contract. Actions without a proven undo path fail closed. Every allow, approval, execution, fallback, and refusal produces a durable invocation record containing the policy rule, route, latency, approval reference, and result evidence. The prototype includes versioned Safety Operator instructions, a reusable proposal-contract skill, and a working Streamable HTTP MCP endpoint exposing only three bounded tools. A harmless NVIDIA Nemotron model request was routed through TrueFoundry and captured in its metrics, demonstrating real model-gateway observability. This is an honest design study. Search, messaging, calendar effects, routes, undo operations, and result IDs are mocked. No production credentials or real external mutations are used. Live demo: https://komina-agent-harness.prodcreat.chatgpt.site Source: https://github.com/sgibler-droid/komina-agent-harness
Built at The Agent Harness Workshop
Komina Agent Harness
Komina Agent Harness is an OpenClaw-native deterministic control layer for autonomous agent tool calls. It grew from real agent workflows: BayDar, a scheduled, stateful agent that searches and evaluates Bay Area events and, under standing delegated rules, can automatically register Stephen for events aligned with his work, discovered the HackerSquad workshop that led to this prototype. In the AI age, automation should help people participate more fully in the world, not remove the human element from it. Stephen built BayDar to find the rooms where he could meet people who shared his passions and develop meaningful relationships. But those events are created by real people and involve identity, schedules, commitments, and personal information. When an agent can make commitments for a person, delegated authority must be explicit and auditable. The same authority problem appears in TestBot's self-directed learning loops, an experiment in memory, autonomy, and machine consciousness, and Komina's public-source cold-case research. These agents may propose valuable actions, but they should not judge their own permission to act. The principle is simple: AI proposes. Code decides. Komina validates the actual tool name and arguments against a server-side, runtime-frozen policy manifest. Safe reads can be allowed automatically. Mutations require a persisted human-approval artifact and a registered mock undo contract. Actions without a proven undo path fail closed. Every allow, approval, execution, fallback, and refusal produces a durable invocation record containing the policy rule, route, latency, approval reference, and result evidence. The prototype includes versioned Safety Operator instructions, a reusable proposal-contract skill, and a working Streamable HTTP MCP endpoint exposing only three bounded tools. A harmless NVIDIA Nemotron model request was routed through TrueFoundry and captured in its metrics, demonstrating real model-gateway observability. This is an honest design study. Search, messaging, calendar effects, routes, undo operations, and result IDs are mocked. No production credentials or real external mutations are used. Live demo: https://komina-agent-harness.prodcreat.chatgpt.site Source: https://github.com/sgibler-droid/komina-agent-harness
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JeremyNeo4j
True Foundry Gateway to Neo4j Aura Agent MCP Tools
I wanted to see if I could frontend a Neo4j Aura Agent via both its external MCP interface AND its external REST API all through TrueFoundry! I can get the MCP to work, but can't find where our OpenAPI spec lives, so didn't create my own MCP from API, yet! I realized that we present our agent as a single tool via MCP, which makes sense, but means I need to work on my Aura Agent system prompt as well as my description to MCP clients or maybe a skill :) Looking forward to perhaps a Neo4j Aura MCP server extension in TrueFoundry. That would allow for creation of Aura db instances, for example. A little different than the Neo4j endpoint I used here. I wonder what can be presented through the REST API too :)
Sologurus
Sologurus
A self-directed language-learning agent that turns a goal into evidence, strategy, and scheduled action.
Deover
Internet Detective
Internet Detective is an evidence-investigation agent that evaluates factual claims using live web sources and returns one of four verdicts: Supported, Contradicted, Unverified, or Insufficient Evidence. The agent examines the exact wording of each claim, investigates up to 10 relevant webpages, prioritizes primary and independent sources, searches for contradictory evidence, and identifies when multiple articles trace back to the same original report. Built with TrueFoundry Agent Builder and MCP Gateway, Internet Detective uses Bright Data for live web search and webpage extraction. It treats retrieved content as untrusted data, detects prompt-injection attempts, and provides an inspectable history of its searches and tool calls. Completed investigations can be saved through the Notion MCP when the user explicitly says “Save investigation.” Each saved record includes the claim, verdict, confidence, evidence, source provenance, source URLs, prompt-injection result, and relevant conversation history. The goal is to turn web research into a structured, transparent, and auditable evidence workflow—showing users not only the conclusion, but also how strongly the evidence supports the exact claim and where that evidence originated.