Learn the language of agentic security

You're shipping agents without a shared language for what can go wrong

You're mid-sprint. You've just wired up a tool call that hits a third-party payments API, and somewhere around the third code review you realize you've never actually defined what "compromise of that API key" means for your system - who can call it, from where, with what scope, and what happens downstream if something goes wrong. You finish the feature. You ship it. The question doesn't go away.

This is not a failure of diligence. It's structural. Agentic architectures introduce trust boundaries that traditional API security thinking doesn't have names for: agent to tool, orchestrator to memory store, agent to agent in multi-hop workflows. The threat-modeling vocabulary that exists was built for enterprise teams with dedicated security hires. At seed stage, that vocabulary either doesn't reach you or arrives wrapped in framework jargon that assumes six months of implementation runway.

Agentic Cyber's event summaries and glossary exist precisely for this gap. We track what's being discussed, debated, and defined at the industry events where agentic security is being worked out in real time - and we maintain a living glossary of the terms that are actually emerging from that work. The goal is that when you and your co-founder sit down to decide whether your agent should have direct database access or go through a read-only wrapper, you're not inventing the vocabulary from scratch.

Six things that change when you have the right terms

Vocabulary is not soft. When you can name a risk precisely, you can scope it, assign it, and decide whether it blocks the sprint or goes on the backlog. Each of these maps to a decision you're probably making right now.

• Shared language with your co-founder. When you both understand what "least privilege" means for an agent's tool call scope - not just in theory but in the context of your specific architecture - the conversation about whether the agent needs write access to production data takes minutes instead of a meandering hour.

• Risk prioritization that fits your stage. You can distinguish between an agent with write access to production data (stop the sprint) and an agent that retrieves read-only records through a sandboxed wrapper (acceptable now, revisit at Series A). That distinction requires terms, not just intuition.

• Architectural patterns with names you can reference. Separating read and write agents, sandboxing tool calls, using memory stores that don't expose raw retrieval outputs back into the prompt - these patterns exist and have names. Knowing the names means you can find prior discussion, compare tradeoffs, and explain your decisions later.

• Faster onboarding for the next engineer. When you hire engineer number three and need to explain why your orchestrator doesn't pass full context to sub-agents, "blast radius containment" is a three-second explanation. Without it, you're re-litigating the architectural decision from first principles.

• Awareness of what's actively being debated in the field. The agentic security space is genuinely unsettled - questions about trust in MCP-based tool registries, about how prompt injection propagates through multi-agent workflows, about what "human in the loop" actually requires architecturally. Our event summaries and glossary cover what is being discussed at industry events.

• A reference point that doesn't assume an enterprise budget. The glossary is written for people building real systems, not for compliance questionnaires.

What this looks like when you're about to add a new tool call

Say you're about to wire up a Stripe integration - your agent needs to fetch transaction history to generate a summary report. Here's the kind of reasoning the glossary and event coverage makes faster.

First question: does the agent need the API key directly, or can it go through a wrapper that logs requests and rate-limits calls? If you know the term "tool call sandboxing", you know this is a solved pattern - and you know what the tradeoff is when you skip it.

Second question: does the agent need write access or read-only? "Least privilege" is a term most engineers know in theory. In agentic systems it has a sharper edge: an agent with broader scope than it needs is an agent that can be manipulated - through a prompt injection in a retrieved document, through a poisoned tool response - into doing something you didn't authorize. A shared term makes that easier to discuss.

Third question: does the orchestrator need to see the raw API response, or just the structured output? If your orchestrator is passing raw Stripe JSON back into the next prompt, you've expanded your attack surface in a way that has a name: OWASP's LLM Top 10 covers the prompt injection vector, but the agentic-specific variant - injection through tool call responses in a multi-step workflow - is something we track as it gets defined and refined in practice. For a practitioner-level look at how teams are solving this, see how Stripe, Snap, and Sondera approach stopping prompt injection without neutering their agents.

The outcome: you ship the Stripe integration with a clear record of what you assessed, what you accepted, and what you deferred. You can explain it to your co-founder in two minutes. When you add the next tool call, the same reasoning takes half the time.

Frequently asked questions

How is this different from just reading OWASP's LLM Top 10?

OWASP is a legitimate starting point - read it if you haven't. But it covers LLM-specific risks: prompt injection, insecure output handling, training data poisoning. It does not cover agentic architecture risks: what happens when agent A can pass data to agent B which has write access to a production API, or how trust is established in an MCP-based tool registry. Those questions are being worked out at industry events right now. That's what we report on and what the glossary reflects.

Is the glossary a way to map our system?

No. The glossary is a reference for agentic-security terms, not a method or a service for mapping your architecture. Agentic Cyber does not analyse your system or build threat models for you. For hands-on help, ask the team about consulting.

What if we're in the "move fast" phase and can't slow down?

Speed at seed is real and legitimate. But agentic systems have a property that traditional software doesn't: the agent makes decisions on your behalf, often against external APIs with real effects. When it "breaks something," the blast radius can include customer data, production writes, or third-party account actions. This isn't about slowing down - it's about making fast decisions with eyes open rather than fast decisions in the dark. For a founder-focused breakdown of how to identify the risks that matter most at your stage, see our guide to agent security risks for AI-native startup founders.

How do we know our own review is working?

Two markers. First: you can draw a diagram of your system and label every trust boundary - agent to tool, orchestrator to memory, agent to external API - and name the risk at each one. Second: when you add a new tool call, the time to assess its security implications is shrinking, because you have a repeatable process instead of starting from scratch each time.

Does this apply to multi-agent systems?

More so. Multi-agent systems add trust boundaries between agents, which is why that area comes up so often in industry discussion.