Overcoming Security and Reliability Barriers to AI Agent Adoption with Fastn UCL

Khalid Muaydh, Founder & CEO, Fastn

Nearly every software company is racing to bring AI agents to market to enable a new class of autonomous workflows for its customers. AI agents add reasoning and adaptation, allowing them to take on decision-making alongside repetitive tasks. More human capacity is freed up for strategy and creativity, which is exactly what enterprise customers are looking for. Given the pace of adoption, companies whose agents can't meet enterprise requirements risk losing to competitors whose agents can.

But meeting those requirements is proving difficult. According to Zapier, 78% of enterprises struggle to integrate AI with their existing systems. SailPoint found that 80% of organizations have encountered risky behaviors from AI agents, including improper data exposure and unauthorized system access. These aren't hypothetical concerns. They're blocking adoption.

To deliver on the promise of agentic AI, agents need to interoperate with customers' backend systems, such as Salesforce, SAP, ServiceNow, and internal databases. These integration points, called tools in agentic AI, must meet enterprise security and compliance requirements while addressing new concerns around prompt injection and agent reliability. Companies that don't address these concerns will see sales stall and deployments blocked.

Agents also struggle to manage context when working with tools. They load unneeded tools and verbose schemas, which bloat their context and cause them to hallucinate. Response times slow down. Token costs climb. Customers end up with agents that are inaccurate, unreliable, and expensive to run.

Consider a company that sells voice agents to other businesses. While serving the SMB market, their integration requirements were light, connecting to integrated suites typical of smaller companies. When this company wanted to move upmarket and serve larger enterprises, it found itself needing to connect to a broader range of SaaS and legacy systems while facing greater scrutiny from IT security teams. The options were to have engineering invest considerably more time in integration and governance infrastructure, or bring in a gateway that met those requirements while improving agent performance.

Fastn helps companies deliver trustworthy and dependable AI agents to their customers. At its core is Fastn UCL, an agentic tools gateway that sits between agents and the MCP servers and APIs they use, handling governance and optimization for enterprise deployments. Many companies have adopted MCP for tool discovery, but MCP only tells agents what tools exist. It doesn't optimize which tools to use for a given task, prevent context from bloating, or enforce security policies. Fastn UCL handles all of that, so software companies can meet enterprise requirements and optimize agent-tool interactions without building that infrastructure themselves.

How Fastn UCL Works

Fastn UCL acts as a hindbrain for agents, autonomously abstracting away tool selection, optimizing context, and enforcing policy so agents can focus on reasoning and completing tasks.

Administrators configure approved toolkits, standardize input/output schemas, create connections, and generate tools for legacy systems through a straightforward interface. The platform is no-code for typical cases, with low-code options when engineers need to tailor behavior for specific requirements.

When an agent needs tools for a task, UCL filters out irrelevant tools and trims unnecessary schema parameters, offering only the tools and required fields needed. Context windows end up about 40% smaller, which directly improves accuracy.

UCL logs tool calls, captures the reasoning behind them, and caches responses. Latency drops by up to 60%. Token costs drop by up to 45%. These numbers come from Fastn's benchmarks and translate to real savings when scaled across customers.

On the governance side, UCL enforces access control, data masking, credential vaulting, and prompt safety on every tool interaction. These capabilities are what enterprise security teams are looking for, and having them makes security approvals far easier to get.

Over time, UCL observes how tools are used and identifies patterns. Common sequences become meta-tools. Instead of three separate tool calls to post a Slack message, there's one. Schemas get normalized, and caching gets smarter, so performance keeps improving the longer the system runs.

UCL provides complete visibility into agent behavior. When something goes wrong at a customer site, support teams can trace tool calls and the execution path, rather than guessing. Faster debugging builds confidence in the system over time.

Fastn UCL's isolation model accommodates the various ways software companies serve their customers. Companies operate separate tenants, each with self-contained workspaces that isolate customer data and control access for support teams and administrators. Each workspace can run multiple agent tool gateways, limiting the scope of systems agents can interact with. End-users have their own contexts per gateway, which manage and cache their backend authorization so agents act appropriately on their behalf.

Fastn UCL is available as a managed service, either multi-tenant for smaller SaaS companies or dedicated for larger ones. For companies whose enterprise requirements dictate a self-contained system, Fastn also offers UCL as self-hosted software.

Customer Experiences with Fastn UCL

HP's Workforce Experience Platform shows what this looks like at scale. HP embedded Fastn so their platform could integrate securely with whatever tools and systems their enterprise customers use. Each customer environment is different, but HP doesn't have to rebuild integration and governance infrastructure for each one. They deliver AI-powered device management that adapts to the customer's environment while meeting security requirements.

Gaurav Roy, VP of Engineering at HP, described it this way: "Fastn has enabled us to connect with our customers' applications and infrastructure, drastically reducing our time-to-market. We've eliminated development bottlenecks and significantly expanded the customer environments we address, connecting many systems more efficiently and improving the scale of the applications we can handle."

Why Fastn

Fastn started as an embedded iPaaS for SaaS companies. Making integrations work reliably across diverse customer environments is what the company has been doing for years. UCL is an evolution of that foundation for agentic AI. The team added governance and context management capabilities that AI agents need to be dependable. UCL builds on the team's core expertise in embedded enterprise integration.

For companies building agents for enterprise customers, particularly in regulated industries or government, they face serious requirements around security, compliance, accuracy, and performance. They can create that infrastructure themselves or use UCL and focus engineering effort on what actually differentiates their product. Their customers get dependable agents they can deploy with confidence. They get to market faster with a competitive advantage.

Try out Fastn UCL for free at fastn.ai.