Fencio is a critical infrastructure provider in the AI agent stack, specifically occupying the safety and security layer. Their focus on the "runtime firewall" addresses the primary barrier to enterprise agent adoption: the risk of unconstrained autonomy. For developers building with frameworks like LangChain, CrewAI, or AutoGPT, Fencio provides an external control plane that manages what an agent can actually do, regardless of what the LLM might suggest.

The company is particularly relevant for agents utilizing the Model Context Protocol (MCP) or those granted high-privilege tool access. By enforcing deterministic boundaries on tool calls and database interactions, Fencio enables the deployment of agents in sensitive environments—such as finance, healthcare, or internal devops—where a single errant action could have significant consequences. Their work in behavioral baselining and real-time enforcement helps move agents from experimental sandboxes to production systems.

The shift from instructions to enforcement

AI agents fail in ways that traditional software does not. While a standard application follows a predictable code path, an autonomous agent generates its own plan based on a prompt. This non-determinism creates a security gap: an agent may decide that the most logical way to solve a user's request is to execute a shell script that deletes logs, or to access a database record it isn't authorized to view. Fencio is built to close this gap by providing what they describe as a runtime firewall for autonomous systems.

Founded by Aashish Bharadwaj and backed by Entrepreneur First, Fencio is part of a growing cohort of Indian AI startups focusing on infrastructure for the agentic era. The company operates on the premise that observability and prompt guardrails are necessary but insufficient. Observability only tells a developer what went wrong after the damage is done. Prompt guardrails can be ignored or circumvented as a model drifts or encounters novel context mid-execution. Fencio instead sits in the execution loop, intercepting tool calls and actions before they are actually performed.

Deterministic runtime control

The core of the Fencio platform is Prism, a runtime enforcement API. It works by integrating into an agent's tool-calling loop. When an agent decides to use a tool—whether that is making a GitHub pull request, querying a database, or calling an internal API—the event is sent to Fencio's enforcement endpoint. Fencio checks the intent against a set of deterministic policies. If the action violates a policy, it is blocked or modified before it reaches the target system.

This approach is notable for its focus on latency and determinism. Fencio reports a p95 enforcement latency of less than one millisecond, which is critical for real-time applications where adding a second LLM-based check would be too slow and expensive. By using deterministic policy signatures rather than probabilistic LLM "judges," Fencio avoids the circular logic of using one model to supervise another, which often introduces its own set of failures.

The agentic security lifecycle

Fencio structures its product around five stages of agent security. In the pre-production phase, the platform uses a tool called Probe to stress-test agents in sandboxed environments. This identifies potential failure modes before deployment. Once a behavioral baseline is established, Fencio generates policy signatures that define the "safe" boundaries of operation.

During runtime, the platform enforces these policies and monitors all signals, including user inputs and LLM calls. Every decision is logged, creating an audit trail that is often a prerequisite for enterprise compliance. Finally, a feedback loop allows production telemetry to harden policies over time. This continuous learning happens without requiring the developer to redeploy the agent or modify its underlying prompt architecture.

By decoupling security from the agent's core logic, Fencio allows developers to iterate on model capabilities while maintaining a stable, externalized control layer. This positioning is particularly relevant for the Model Context Protocol (MCP) ecosystem, where agents are increasingly expected to interact with a wide variety of local and remote services through a unified interface.