Technicity is highly relevant to the agent ecosystem as a specialist in evaluation and reliability for multi-step agentic workflows. They move beyond the simple 'evals' used for text generation, focusing instead on the sequence of actions and decisions an agent takes over time. Their multi-turn testing framework directly addresses the 'agent drift' problem where a system might become less accurate or safe as a session progresses.
They occupy the infrastructure and services layer of the agent stack, helping companies move agents from a demo state to production. By championing a pragmatic, testing-first approach to agent design, Technicity provides the tools necessary for enterprises in regulated sectors to adopt agentic workflows without assuming unmanaged risks.
Technicity is a firm that occupies the space between high-level AI research and the practical requirements of enterprise software. Founded in 2014, the company spent its first decade building its reputation in the internet of things (IoT) and connected products. This background is significant. In the IoT world, software interacts with physical reality, and the costs of failure are high. This history in hardware and regulated industries—where failure has physical consequences—informs their current approach to AI agents.
The company's current focus is the development and implementation of agentic AI. Unlike standard chatbots that provide a single response to a single prompt, agentic systems are designed to execute multi-step tasks, interact with external APIs, and make decisions over time. This complexity introduces new failure modes that standard testing tools are often unequipped to handle. Technicity addresses this through a multi-turn, agentic testing framework. This framework is built to stress-test how an AI system maintains its logic and safety guardrails over a long sequence of interactions.
One of the company's notable projects, a connected boating platform, illustrates their experience with complex data environments. That project required managing remote views of boat components and operating systems, a task requiring high reliability and real-time accuracy. They have carried this requirement for precision into their AI work, where they now help enterprises build agentic workflows that can navigate similar levels of environmental complexity. This is a shift from the common practice of building prototypes toward building production-ready systems.
The firm positions itself as a pragmatic alternative to the hype-driven side of the AI market. They argue that the primary hurdle for AI in the enterprise is not a lack of intelligence in the models themselves, but a lack of infrastructure to ensure those models behave predictably. This focus on responsible AI is not just a marketing slogan for Technicity; it is a technical requirement. Their testing framework is specifically designed to detect when an agent might deviate from its intended goal or provide unreliable information in a business-critical context.
Based in the consultancy space but maintaining a product-like focus with their testing frameworks, Technicity occupies a specific niche. They are not competing with the providers of large language models like OpenAI or Anthropic. Instead, they are part of the essential ecosystem of companies making those models usable for businesses. Their team, which remains relatively small at 11 to 50 employees, focuses on deep engagements where they act as architects of innovation.
Faisal Khan and the leadership team frequently emphasize the importance of context. In their view, the future of AI is not about bigger models but about better-scoped agents that understand the specific nuances of a business's data and operations. This focus on the agentic stack—moving from passive retrieval to active, tool-using agents—places Technicity at a specialized point in the current enterprise AI trajectory.
A framework for ensuring the reliability of long-running AI agent interactions.
Technicity is hiring.