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@havenhaisalonquan1: Bao lâu bạn chưa cắt tỉa lại tóc rồi. #havenhairsalon #tiemtocquan1

HAVEN HAIR SALON- Quận 1
HAVEN HAIR SALON- Quận 1
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Saturday 13 June 2026 02:28:05 GMT
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An agent burns tokens, declares the task complete, and then fails the tests. That is often an architecture problem, not a prompting problem. When an agent underperforms, the usual reflex is to rewrite the prompt or switch to a stronger model. But many failures actually come from the system around the model, and different problems need to be solved at different layers. 𝟭. 𝗟𝗼𝗼𝗽: repeats until evidence says stop The loop is the smallest unit of agency. The agent acts, checks the result, and either stops or tries again. The important part is how completion is decided. A reliable agent should not stop simply because the model believes the work looks correct. It should stop when there is external evidence, such as a passing test, a successful build, a validated output or another measurable condition. Without this verification loop, an agent can confidently declare success while the task is still incomplete. 𝟮. 𝗚𝗿𝗮𝗽𝗵: decides what runs next A loop decides whether execution should continue. A graph decides where execution should go next. It defines branches, retries, specialist-agent handoffs, fallback paths and shared state. Once a workflow has multiple possible routes, the graph makes those routes explicit, inspectable and controllable. This is what turns repeated execution into a structured agent workflow. 𝟯. 𝗛𝗮𝗿𝗻𝗲𝘀𝘀: gives the model an operating environment The model provides reasoning, but the harness determines what that reasoning can actually do. It defines the tools, APIs, files, memory, permissions, context, logging and execution environment available to the model. That distinction matters because model capability and agent capability are not the same thing. A model may understand exactly how to solve a task, but if the required tool, data source or permission is not exposed through the harness, the agent still cannot complete it. A better prompt cannot compensate for a missing capability. 𝟰. 𝗠𝗲𝘁𝗮-𝗵𝗮𝗿𝗻𝗲𝘀𝘀: governs multiple agent harnesses This layer becomes important when teams are using Claude Code, Codex, internal agents and specialised domain agents together. Each may have its own tools, sessions, policies, permissions and execution environment. A meta-harness creates a common layer across them for orchestration, governance, isolation, shared policies and movement of context or workflows between different agents. Omnigent is one open-source implementation of this layer, designed to provide a governed environment across different agent harnesses. 𝗧𝗵𝗲 𝗱𝗶𝘀𝘁𝗶𝗻𝗰𝘁𝗶𝗼𝗻 𝗶𝘀 𝘀𝗶𝗺𝗽𝗹𝗲: Loop makes the work verifiable. Graph makes the workflow structured. Harness makes the model operational. Meta-harness makes multiple agent environments governable. A stronger model can improve reasoning, but reliable agents depend just as much on the architecture built around the model.
An agent burns tokens, declares the task complete, and then fails the tests. That is often an architecture problem, not a prompting problem. When an agent underperforms, the usual reflex is to rewrite the prompt or switch to a stronger model. But many failures actually come from the system around the model, and different problems need to be solved at different layers. 𝟭. 𝗟𝗼𝗼𝗽: repeats until evidence says stop The loop is the smallest unit of agency. The agent acts, checks the result, and either stops or tries again. The important part is how completion is decided. A reliable agent should not stop simply because the model believes the work looks correct. It should stop when there is external evidence, such as a passing test, a successful build, a validated output or another measurable condition. Without this verification loop, an agent can confidently declare success while the task is still incomplete. 𝟮. 𝗚𝗿𝗮𝗽𝗵: decides what runs next A loop decides whether execution should continue. A graph decides where execution should go next. It defines branches, retries, specialist-agent handoffs, fallback paths and shared state. Once a workflow has multiple possible routes, the graph makes those routes explicit, inspectable and controllable. This is what turns repeated execution into a structured agent workflow. 𝟯. 𝗛𝗮𝗿𝗻𝗲𝘀𝘀: gives the model an operating environment The model provides reasoning, but the harness determines what that reasoning can actually do. It defines the tools, APIs, files, memory, permissions, context, logging and execution environment available to the model. That distinction matters because model capability and agent capability are not the same thing. A model may understand exactly how to solve a task, but if the required tool, data source or permission is not exposed through the harness, the agent still cannot complete it. A better prompt cannot compensate for a missing capability. 𝟰. 𝗠𝗲𝘁𝗮-𝗵𝗮𝗿𝗻𝗲𝘀𝘀: governs multiple agent harnesses This layer becomes important when teams are using Claude Code, Codex, internal agents and specialised domain agents together. Each may have its own tools, sessions, policies, permissions and execution environment. A meta-harness creates a common layer across them for orchestration, governance, isolation, shared policies and movement of context or workflows between different agents. Omnigent is one open-source implementation of this layer, designed to provide a governed environment across different agent harnesses. 𝗧𝗵𝗲 𝗱𝗶𝘀𝘁𝗶𝗻𝗰𝘁𝗶𝗼𝗻 𝗶𝘀 𝘀𝗶𝗺𝗽𝗹𝗲: Loop makes the work verifiable. Graph makes the workflow structured. Harness makes the model operational. Meta-harness makes multiple agent environments governable. A stronger model can improve reasoning, but reliable agents depend just as much on the architecture built around the model.
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