Build and secure systems that act on their own. This track covers agentic AI design, tool use and multi-agent orchestration alongside the new attack surface — and governance — that autonomy introduces.
Autonomous Agents Act — Including When They're Wrong
Agentic systems don't just answer questions; they take actions with real privileges — calling tools, running code, and moving data and money on your behalf. A single prompt injection can turn that autonomy into tool misuse, data exfiltration or unintended transactions, all without a human ever clicking "confirm". Governance and security practice are lagging far behind how fast agents are being shipped into production.
A blend of agent design and adversarial thinking your engineers can apply immediately — every highlight below maps to a module in the detailed course syllabus.
Agent architectures — planning, tool use, memory and multi-agent orchestration — and where they fail
The agent attack surface — prompt injection, tool abuse and the OWASP Top 10 for LLM Applications
Securing tool use and multi-agent systems against injection and untrusted content
Guardrails, sandboxing and least-privilege patterns for safe, bounded autonomy
Governing autonomous systems — approvals, audit trails and clear accountability for agent actions
Three delivery depths — from a leadership briefing to a full hands-on lab — all tailored to your agent stack and team.
A leadership-level session on what agentic AI can do, where the risk lives and how to govern it — no coding required.
For AI and security engineers: agent architectures, the attack surface, guardrails and tool-use hardening, with guided demos.
Full immersion: build a tool-using agent, break it with prompt injection, then harden it with guardrails, sandboxing and least privilege.
Conceptual AI awareness is enough for the executive briefing — no coding or model experience needed
Familiarity with LLM APIs and how prompts and tools work for the technical deep-dive
Comfort with one programming language (Python, JavaScript/TypeScript, Go or similar) for the hands-on lab
A hosted agent lab with tool-using agents ready to run — no local setup needed
Deliberately vulnerable agent demos that show prompt injection and tool abuse first-hand
A guardrails and threat-model template your team keeps and reuses
Content is pitched to each audience so builders, defenders and leaders all leave with what they need.
Design and ship the agents, tools and orchestration that production systems will rely on.
Leave able to build capable agents with guardrails, sandboxing and least-privilege tool access baked in.
Threat-model, test and defend the new attack surface that agents introduce.
Leave able to red-team agents for prompt injection and tool abuse, and to set the controls that contain them.
Decide how much autonomy to grant, set the guardrails and own the accountability.
Leave with a governance model, a threat-informed roadmap and clear approval and audit patterns for agent actions.
Capabilities and tangible artifacts that translate directly into safer agentic systems.
A shared, accurate mental model of how agents act — and where that autonomy becomes risk
The ability to spot prompt injection and tool-abuse paths before they reach production
Confidence to design guardrails, sandboxing and least-privilege tool access
A repeatable way to govern, monitor and hold autonomous systems accountable
An agent threat model covering prompt injection, tool abuse and untrusted content
A tool-use and permissioning policy that scopes what each agent is allowed to do
A guardrail and sandboxing reference for containing agent actions
An evaluation and red-team plan for testing agents before and after release
A QSECS certificate of completion for every participant
Five modules scaling from agent foundations to governing autonomous systems. Select a module to expand it.
From single prompts to autonomy — planning, acting and looping until a goal is met.
Reasoning loops, tool calling, memory and retrieval, and how the pieces fit together.
Planner-worker, supervisor and collaborative patterns — and where coordination goes wrong.
Runaway loops, hallucinated actions, unclear ownership and the limits of today's models.
Mapping the four surfaces — the model, the prompt, the tools and the data it touches.
Direct and indirect injection through documents, web pages and tool output — and why it's hard to fix.
How injected instructions turn legitimate tools into exfiltration and unintended actions.
Using the OWASP Top 10 for LLM Applications as a checklist for agentic risk.
Treating tool output as untrusted and separating instructions from data.
Input validation, output filtering and scoping what each tool can actually do.
Containing injection that propagates between agents and securing inter-agent messages.
Hardening RAG and browsing so external content can't hijack the agent.
Input, output and action guardrails — what they can and can't catch.
Isolating tool execution and side effects so a compromised agent stays contained.
Scoping credentials, permissions and blast radius for every tool an agent can call.
Where to require confirmation and how to add kill-switches for high-impact actions.
Logging agent reasoning, tool calls and actions so behaviour can be reviewed.
Building evals and adversarial tests that catch unsafe behaviour before release.
Assigning ownership for agent actions and aligning with emerging AI governance.
Threat-model, attack and then harden a tool-using agent, and present the result.
Awarded by QSECS · Quantum Security Solutions
Every participant who completes the track receives a verifiable QSECS Certificate of Completion — a credible signal to leadership, auditors and customers that your teams can build and secure autonomous systems responsibly.
Individually issued with a unique, verifiable credential ID
Hands-on and lab tracks include a graded capstone assessment
Maps to continuing-education (CPE) hours for common security certifications
Shareable to LinkedIn and your internal skills matrix
An illustrative Day 1 from the 2-day format — every agenda is tailored to your goals before delivery.
What changes when an LLM stops answering and starts acting — and why that reshapes the threat model.
Reasoning loops, tool calling, memory and multi-agent orchestration — with worked examples.
Prompt injection, tool abuse and excessive agency, mapped to the OWASP Top 10 for LLM Applications.
Designing the controls that keep a capable agent bounded and contained.
Break a tool-using agent with prompt injection, then add guardrails and watch the attack fail.
Day 2 covers securing multi-agent systems, building an evaluation and red-team plan, and a governance-and-accountability workshop.
Everything teams usually ask before booking the agentic AI security track.
No deep background is required. The executive briefing only assumes conceptual AI awareness. The technical deep-dive expects familiarity with LLM APIs, and the hands-on lab assumes comfort with one programming language. We send a readiness checklist beforehand so everyone arrives at the right level.
The track is model- and framework-agnostic. We teach patterns — reasoning loops, tool calling, orchestration, guardrails and least privilege — that apply across the major LLM providers and agent frameworks, and we adapt examples to whatever stack your team uses.
All three. We run sessions in-person at your site, fully remote, or hybrid — across time zones for distributed teams. The hands-on labs run in a hosted agent sandbox so delivery mode never changes the experience.
We provide a hosted lab with tool-using agents and deliberately vulnerable demos. Participants attack them with prompt injection and tool abuse, then add guardrails, sandboxing and least privilege and confirm the attacks fail. Everything runs in a browser — there's no local setup, and nothing touches your production systems.
Yes. We tailor examples and labs to your frameworks, models and tools, and can anchor the threat-modelling workshop to your real agent architecture under NDA. Tailoring is scoped during the requirement-analysis call.
Yes — every participant receives a verifiable QSECS Certificate of Completion, and hands-on tracks include a graded capstone. The credential maps to CPE hours for common security certifications.