JIT-Agent/jit-27b

VISIONPricing:Input $1.6 / Cached $0.15 / Output $12Concurrent Unit Cost:2Model Size:27BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Aug 17, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Featherless Exclusive Cold

JIT-Agent/jit-27b is a 27.36 billion parameter harness intelligence model developed by JIT-Agent, built upon Qwen/Qwen3.6-27B. It specializes in synthesizing executable, task-conditioned agent harnesses, generating operational scaffolds for other foundation models to manage memory, planning, actions, and tool orchestration. This model is designed to write the system through which an agentic model operates, rather than directly solving tasks.

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JIT-Agent-27B: Harness Intelligence Model

JIT-Agent-27B is a 27.36 billion parameter model, based on Qwen/Qwen3.6-27B, designed to generate executable agent harnesses. Unlike traditional LLMs that directly solve tasks, JIT-Agent focuses on creating the underlying system for other foundation models. It synthesizes complete operational scaffolds, dictating how an agent maintains memory, forms plans, executes actions, and orchestrates tools.

Key Capabilities

  • Agent Harness Generation: Produces task-conditioned harnesses, including four Python modules (memory.py, planning.py, action.py, tool_policy.py) and one prompt.yaml configuration.
  • Modular Agent Design: Generates structured output for managing agent components like memory construction, plan state management, task execution loops, and tool exposure.
  • High Context Length: Built on an architecture supporting 262,144 tokens, with a recommended serving context of 163,840 tokens.
  • Research-Oriented: Intended for research in areas such as task-adaptive agent architecture generation, model-harness co-design, and modular agent runtimes.

Good For

  • Developing Agentic Systems: Ideal for researchers and developers building complex AI agents that require dynamic, task-specific operational structures.
  • Automating Agent Orchestration: Useful for generating the code that defines an agent's behavior, memory, and tool use based on natural language descriptions.
  • Exploring Agent Architecture: Provides a platform for experimenting with different compositions of memory, planning, action, and tool policies within agent frameworks.

This model is specifically a harness generator and requires the JIT-Agent runtime for proper usage, including prompt construction, output validation, and execution against an off-the-shelf agentic model. Direct chat-style prompting is not its intended use.

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