November 2026 Summaries

An agent harness is the software layer that turns a language model into a goal-oriented agent by managing iterative model calls, tools, context, state, error recovery, output validation, and stopping conditions while connecting the model to relevant data. Unlike a model, which generates text and selects next actions, or a runtime, which provides execution infrastructure and security constraints, the harness coordinates how actions are carried out; an agent combines the model, accessible data, and harness. Core harness capabilities include prompt and context assembly, tool execution, memory, retries, guardrails, sub-agent coordination, and task completion controls, while frameworks such as LangGraph provide components for building harnesses rather than functioning as complete harnesses themselves. Enterprise deployments additionally require inherited per-user permissions, narrowly scoped tools, externally enforced rules, approval processes, auditing, evaluation, rollback, and model flexibility. Organizations may build custom harnesses for product-specific needs or use managed platforms for internal agents, and Credal is presented as a platform that provides managed agent-building capabilities and governed MCP-based data access for both hosted and custom harnesses.