Indian enterprises are increasingly investing in AI agents—software that autonomously manages tasks such as booking meetings, filing reports, and executing complex business workflows. As global tech giants like Microsoft and Salesforce, along with a wave of startups, rush to integrate these capabilities into their software, the less-discussed component driving this race is the agentic harness. This software framework not only supports AI models but also enables them to act autonomously rather than merely respond to queries.
While AI models excel at simple tasks, such as answering straightforward questions, they often struggle with complex, multi-step processes. They lack the ability to remember previous interactions, manage tasks over extended periods, or recover from errors without human intervention. This is where the agentic harness becomes essential. It functions as a crucial software layer that surrounds an AI model, managing persistent memory, providing access to necessary tools, implementing safety protocols, and overseeing multiple tasks simultaneously. Essentially, harnesses enhance the reliability and efficiency of AI models.
To visualize the function of an agentic harness, think of it as an operational layer that connects the AI model to the real world. It manages memory—an ongoing issue for Large Language Models (LLMs)—ensuring that an agent does not forget its initial objectives after hours of operation. The harness also orchestrates the use of various tools, determining their sequence, error-handling protocols, and task management. Moreover, it allows for human oversight, addressing concerns about AI functioning uncontrollably. Agents within a harness are designed to require human approval for critical actions, such as data deletion or bulk emailing.
Globally, several entities are actively developing agentic harnesses. OpenClaw, an open-source project, has emerged as a significant player by providing LLMs with features like scheduling, browser control, and persistent memory. Similarly, Anthropic has created Claude Code, a harness designed to enhance the functionality of its Claude AI model for professional development tasks. Microsoft’s AutoGen focuses on conversational multi-agent systems where agents interact and validate each other’s outputs. American startup LangChain has introduced LangGraph to manage sophisticated multi-agent workflows, while IBM’s watsonx Orchestrate addresses the needs of regulated enterprises by ensuring governance and audit trails.
In India, IT firm Hexaware has launched Agentverse, a platform boasting over 600 ready-to-deploy agents for enterprise IT and business operations. However, industry experts caution that many solutions in the market may lack depth, with numerous offerings considered “thin wrappers” that claim to be agentic but offer limited capabilities.
Safety is a paramount concern when it comes to deploying AI agents. The harness provides an essential security framework by clearly delineating the permissible actions for an agent prior to operation. It specifies what data the agent can access, which APIs it can utilize, and which actions necessitate human consent. Each tool interaction is logged for audit purposes, ensuring transparency. Importantly, rather than relying on probabilistic behavior, the harness incorporates hard-coded controls that delineate when human oversight resumes. A notable emerging standard in this domain is Anthropic’s Model Context Protocol (MCP), which standardizes tool and context sharing between agents and external systems.
Looking ahead, trends suggest that while AI models may become commoditized, the development of robust harnesses will serve as a competitive advantage for companies. According to a report by Deloitte, the agentic AI market is projected to reach $35 billion by 2030. The next phase is expected to involve a shift to human-on-the-loop orchestration, where human operators will set policies and monitor outcomes rather than approving each individual action. Deloitte noted that businesses are likely to accelerate their exploration and scaling of complex agent orchestrations while keeping human supervision in place over the next 12 to 18 months.
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