Tech Navigator: The agentic AI gateway

Insights

• Agentic traffic needs to be secure, routed properly, and traced effectively, with agent decision-making explained with sufficient reasoning for high-risk business processes.
• The agentic AI gateway provides this, the missing layer for secure AI integration.
• Here we provide the architecture for this gateway, including the semantic router, governance engine, tool and protocol adapter, and observability plane.
• Six recommendations for building the agentic gateway, based on our client implementations, include adopting a protocol-first strategy, centralizing security, and treating agents as identities.
• As interactions evolve from human browsing to agent execution, this agentic AI gateway can also act as an enterprise’s storefront, enabling other external agents to transact with the business.

While the shift to agentic AI promises gains in productivity and the ability to automate business processes, current infrastructure is inadequate to deal with the requirements of AI agents. Agents look up databases, communicate with each other, query tools, execute actions, all within the perimeter of the enterprise. This traffic needs to be secure, routed properly, depending on business needs, and transparent enough so that decisions made by agents can be traced effectively and decision-making explained with sufficient reasoning for high-risk business processes.

The agentic AI gateway provides this. A sort of governed collaboration plane, the agentic AI gateway doesn’t just manage traffic, like other legacy gateways, but ensures that agents have enough memory, context, and organizational understanding to query databases, tools, and other agents in a secure and cost-effective fashion.

This leads to organizations missing out on the coordination needed for end-to-end process efficiency and disciplined investment roadmaps.

The agentic AI gateway is the missing layer for secure AI integration, especially as organizations move from experimental pilots to agentic AI at scale.

Traditional infrastructure isn’t enough

One of the difficulties in agentic AI is the explosion of connections as agents connect to tools and other agents. In traditional architecture, connecting an agent to a tool meant writing custom code or providing an application programming interface (API) wrapper for that specific agent framework. This leads to a maintenance nightmare where developers spend more time maintaining integrations than improving agent reasoning.

API gateways like NGINX, Kong, and Apigee were engineered for short-lived requests: they process a request, route it, return a response, and immediately forget the interaction. Agentic processes, on the other hand, often involve a multiturn conversation and reasoning process that could last for hours. The agentic infrastructure must maintain sessions and memory across long-running interactions so that the reasoning process can persist across organizational workflows.

Further, API gateways route traffic based on URL paths, assuming the URL tells the system exactly what the user wants, whereas agentic routing uses natural language for intent-driven workflows and is both probabilistic and semantic. The gateway must understand the intent of the prompt, capabilities that path-based routers lack.

In addition, API gateways don’t understand agent economics, where tokens, not requests, are the unit of value. A single API call could consume 50 tokens or 50,000 tokens, depending on the conversation context sent with each message, yet a traditional gateway treats both the same, potentially burning through monthly budgets in hours.

Finally, traditional infrastructure treats traffic as binary payloads, where some traffic is allowed through the system while other traffic is stopped based on security requirements. But agentic AI requires the infrastructure to inspect many facets, including monitoring for leakage of sensitive personal data, toxicity, and hallucinations in real time as tokens are generated. This requires high-performance parsing that traditional gateways can’t handle without introducing unacceptable latency.

Key metrics to be tracked for different gateways

However, the agentic AI gateway is just one of three routes an organization can take when adopting modular and distributed AI ecosystems. Along with the agentic AI gateway, there is also the more simple AI gateway and the model context protocol (MCP) gateway. The AI gateway, a control layer that manages and secures large language model (LLM) and API usage using routing, rate limits, and observability is often used for cross-cutting concerns like rate limiting, logging, and model routing. The MCP gateway — a gateway that exposes tools and data to AI systems through the MCP so agents can access external capabilities — is often used when standardizing access to tools is the major objective. These are often chosen when organizations don’t need planning, tool orchestration, or autonomous workflows that the agentic AI gateway provides.

Although each of these gateway types serves a different architectural purpose, they share a common responsibility, namely, managing the nonfunctional requirements (NFRs) that ensure agentic systems remain performant, reliable, secure, and cost‑effective at scale.

NFRs such as latency, throughput, reliability, error handling, governance enforcement, and traffic control become even more crucial as workloads evolve from simple model inference calls to complex tool orchestration and multiagent reasoning loops. Tracking these NFRs consistently across gateway types provides architectural clarity and helps engineering teams maintain predictable service quality, control operational risk, and optimize resource consumption.

By aligning metrics like performance, efficiency, or reliability side‑by‑side, teams can better evaluate tradeoffs, select the right gateway for their workload, and ensure that NFRs are embedded into design, monitoring, and operational review processes.

The agentic AI gateway architecture

Enterprises face several challenges when adopting the agentic AI gateway. Giving AI agents access to systems creates security and compliance risks, while new types of attacks and differing security rules across AI providers make consistent oversight difficult.

The agentic AI gateway is needed when organizations want to manage everything an agent does from start to finish and make the transition from agentic chaos to a disciplined agentic mesh. The mesh is a network of autonomous AI agents that securely and dynamically coordinate, communicate, and collaborate across tasks, tools, and environments.

An agentic AI gateway also mitigates the issues of how managing many AI services becomes operationally complex, especially when tracking performance and reliability. Older legacy systems often do not work easily with AI-driven tools, and different providers use incompatible approaches, which complicates integration and switching. At the same time, AI usage can quickly drive up costs and make return on investment (ROI) hard to predict. Also, organizations must address skills gaps and put safeguards in place, so humans remain involved in important decisions.

The component architecture for the agentic AI gateway that Infosys has used in client implementations is composed of four layers:

Layer 1 - Semantic router: This component replaces traditional routing; it matches the user’s or agent’s intent to available agents and dynamically selects the optimal LLM for a given task based on cost, latency, and performance.

Layer 2 - Governance engine: This is the security enforcement point. It executes a chain of guardrails on every interaction. For instance, it sanitizes prompts, scans and redacts sensitive data before it is sent to a model provider. It also inspects generated content for hallucinations, bias, or data leakage.

Layer 3 - Tool and protocol adapter: This layer solves the integration challenge. It hosts a tool registry – a catalog of approved enterprise APIs wrapped in the MCP. When an agent connects, the gateway dynamically exposes the tools that the agent is authorized to use, and the agent sees these tools as native functions, regardless of the underlying backend software.

Layer 4 - Observability plane: The gateway provides more than just logs. Rather, because agents are nondeterministic, or can produce different outcomes even with the same starting conditions, the gateway records the reasoning steps and tool calls, enabling developers to understand why an agent made a decision. This plane also captures the full state of an interaction so that it can reproduce and diagnose failures in complex, multiturn conversations.

Protocols for agentic collaboration

The success of the agentic AI gateway relies on its ability to support new and evolving open protocols. These include MCP, which enables agents to access external data sources and systems through a single, consistent interface; the agent-to-agent (A2A) protocol, allowing agents to interact, share information, and coordinate tasks with other AI agents; and Google’s universal commerce protocol (UCP), a common language for platforms, agents, and businesses compatible with the agent payments protocol (AP2) ecosystem. These protocols are the TCP/IP of the agentic era, enabling interoperability and preventing vendor lock-in, allowing the platform to be poly-AI.

While core MCP and A2A protocols laid the foundations for semantic interactions, they opened the possibilities for business domain-specific protocols such as UCP and AP2. Just as REST APIs resulted in the development of the API economy on the web, these new protocols are laying the foundations of a new agent economy.

As this plays out, enterprise gateways will need to evolve in their roles as the guardians of the enterprise.

The agentic gateway as the security and quality control plane

The agentic gateway must also provide centralized security, not just from a traditional authentication and authorization perspective, such as providing identity and access validation, but also from a semantic validation and explainability perspective, ensuring that the agent’s actions are appropriate and safe for the specific situation. This requires deep inspection and observation capabilities that should be built into the control plane of the gateway using a zero-trust architecture.

Agent identities must be validated before access to systems is approved, with the gateway enforcing capability-based access control; for instance, one agent is allowed to read the invoice, but isn’t allowed to delete anything. Another innovation is the centralized guardrails engine: this scans prompts for malicious patterns and uses natural language processing to detect social engineering and prompt injection attacks, while also filtering responses for hazardous content.

For situations where decisions made by agents are under contention, the gateway can provide a reasoning trace so that decisions can be traced back to the specific prompt, context, and tool output that influenced the agent. These logs are stored in tamper-proof databases, essential for compliance with regulations like GDPR and the EU AI Act.

Agents can degrade over time as the underlying models change or the data environment shifts. The gateway monitors for semantic drift, detecting if an agent’s success rate or reasoning quality is deteriorating, and also tracks key metrics like goal completion rate, tokens per task, and latency, triggering alerts or automated rollbacks if thresholds are breached.

Intelligence within the gateway

Gateways themselves are evolving to embed AI within their capabilities. For example, an LLM can be used within the gateway to perform request-response transformation — modifying the input and the output returned to work with tools, models, and downstream services — threat detection and response, and dynamic load balancing. While some of these capabilities are new and evolving, enterprises must evaluate these in the context of the use case, considering latency, the level of real-time decision-making needed, as well as the cost-risk benefit.

Six recommendations for building the agentic gateway

Based on our client implementations:

1. Deploy the collaboration plane: Do not allow agents to be built in silos. Establish the gateway as a mandatory entry and exit point for all agentic traffic. This provides immediate visibility and prevents shadow AI in the organization.
2. Adopt a protocol-first strategy: Standardize on secure MCP for all internal tool integrations. Mandate that every new internal API or database be exposed by an MCP wrapper. This decouples data from specific agent frameworks, and future-proofs architecture. Also, evaluate A2A for cross-departmental workflows.
3. Centralize security: Move security policies out of application logic and into the gateway layer. Security must be centralized, policy-based, and enforced consistently. The gateway should be able to catch threats in real time.
4. Architect for asynchrony: Use the gateway to bridge the synchronous world of human interaction with the asynchronous world of agent execution.
5. Treat agents as identities: Implement identity governance for agents. Use the gateway to enforce zero-trust access control. Every agent must have a verifiable identity and a strictly scoped set of capabilities, and no agent should have ungoverned access to enterprise data.
6. Prepare for the agentic economy: The agentic web, where AI agents act on behalf of people and businesses, is coming. To prepare, expose public catalogs and services via UCP endpoints on your gateway. Ensure that third-party agents can interact with your enterprise.

A gateway into the future

The agentic AI gateway is a strategic control plane for an AI-enabled enterprise. It secures and governs the evolving agent access layer for robust and safe agentic communication, while still governing the existing API surface by which traditional systems and humans interact with the enterprise.

Establishing this governed collaboration plane moves organizations from pilots to business value. The convergence of semantic gateway architectures with protocols like MCP and UCP provides the blueprint for this transformation.