In the rush to adopt artificial intelligence, enterprises have been busy asking what they will be able to achieve with AI capabilities. But agentic AI—a form of artificial intelligence that can autonomously plan, make decisions, and take actions toward a goal—is here now. And that means it’s time to start asking what these AI solutions might do next (with or without being told).
Agentic AI marks a fundamental shift in how systems behave: it’s the “what next?” that we’ve all been waiting for since generative AI took center stage. These aren’t static tools following rules or even reactive engines responding to prompts. They are autonomous agents, AI models capable of reasoning, making decisions, and acting on goals with minimal human intervention or even oversight. And by 2028, they will be involved in around a third of GenAI interactions.
This brings efficiency gains that exceed even generative AI. But it also introduces a very real risk: unpredictable execution at machine speed, across cloud environments and enterprise data stacks.
Why Are AI Agents Keeping Security Professionals Up At Night?
For CISOs, this is a nightmare scenario: systems that write code, schedule actions, make purchases, or expose vast amounts of data, all without a deterministic, traceable command. Unlike conventional apps, agentic AI doesn’t operate on “If X, then Y.” AI agents operate on “Here’s the goal. I’ll figure out how.”
And if that “how” isn’t locked down with rigorous safeguards, you’re no longer just dealing with misconfigurations. You’re looking at exfiltration, escalation, and the potential automation of attack paths at scale.
So before we embrace the promise of agentic AI, we need to confront its security implications head-on.
Agentic AI vs Traditional AI vs Generative AI
Most teams are already familiar with traditional AI, which relies on rule-based logic to automate decisions. By now, almost all of those same teams are using generative AI, which can produce new content based on probabilistic patterns. But agentic AI introduces a third dimension: autonomy.
AI agents gather context from their environment. Unlike previous generations, they pursue objectives on their own, and adapt in real time. That changes the genAI threat model we’ve been coming to terms with for the last 2 years.
Below is a breakdown of how these approaches compare:
How Does Agentic AI Work?
Agentic AI systems are built to operate beyond one-off tasks. Instead of reacting to individual prompts, they autonomously plan, act, and adapt based on goals. This capability hinges on a modular architecture that combines multiple components into what’s often called an agent loop or perception–action cycle.
Let’s break down the core components of how agentic AI functions:
1. Goal Specification
An agent starts with a high-level objective. This could be user-defined (“book me the cheapest flight to New York this week”), or the agent might infer from context. The key distinction is that the agent interprets the goal and determines how to accomplish it without explicit step-by-step instructions.
2. Planning & Decomposition
Once a goal is defined, the agent breaks it down into discrete sub-tasks. This is typically powered by:
- LLMs or symbolic planners (e.g., ReAct, Tree-of-Thoughts, or AutoGPT frameworks).
- Task graphs that sequence dependencies.
- Tool selection algorithms that decide whether external plugins, APIs, or internal functions are required
This phase is where a simple goal becomes a dynamic execution plan, potentially with dozens of downstream operations.
3. Tool Use & API Invocation
Agentic systems often integrate toolformer-style architectures, allowing them to:
- Call external APIs (e.g., calendar services, search, CRM systems).
- Query databases.
- Trigger workflows across cloud platforms or internal enterprise systems.
Each API call becomes a step in the agent’s strategy, and each response informs its next move. This tight feedback loop is powerful, but also dangerous if left unsupervised.
4. Environment Perception
Advanced agents maintain state awareness, tracking context across sessions and dynamically adjusting based on:
- New user inputs.
- External events (like API failures or changes in database entries).
- Previously executed actions and their outcomes.
This ability to perceive and adjust gives agents the resilience and flexibility of a human operator, but at machine speed and scale.
5. Memory & Adaptation
Unlike stateless models, agentic systems often include:
- Short-term memory for recent interactions.
- Long-term memory for storing learned behavior or past strategies.
- Reinforcement learning or feedback loops for self-tuning over time.
This creates a system that learns how to act better, which exponentially increases both its usefulness and the size of the attack surface.
Key Features of Agentic AI
Agentic AI systems go beyond reactive language generation. They possess structural capabilities that allow them to operate as autonomous decision-makers within complex environments. Here are the five foundational features that define agentic AI:
Autonomy: Agentic AI can initiate actions without explicit human prompts. Once given a high-level goal, it independently determines what to do next, selecting tools, planning steps, and driving execution.
Contextual Awareness: These systems maintain memory of previous interactions, environmental states, and ongoing goals. This lets them make decisions informed by past outcomes, user history, or external signals, crucial for multi-turn workflows and long-horizon tasks.
Reasoning and Planning: Rather than responding impulsively, agentic AI decomposes goals into subtasks, sequences them logically, and adjusts plans as conditions change. Techniques like ReAct, Tree of Thoughts, or symbolic planning often underpin this behavior.
Learning and Adaptation: Agents evolve over time. Through feedback, outcome tracking, or fine-tuned memory mechanisms, they refine their strategies, tools, and decisions, potentially improving performance with each iteration.
Action Execution: Agentic AI can invoke tools, APIs, or even control systems directly. This is what makes AI agents capable of triggering workflows, querying databases, or sending emails.
Agentic AI Use Cases
Agentic AI enables entirely new categories of automation and decision-making. Unlike traditional systems, which rely on pre-programmed flows, agentic AI can navigate ambiguity, pursue goals across multiple steps, and adapt dynamically to changing inputs and complex tasks.
Here are three real-world domains where agentic systems are starting to have a transformative impact:
Robotic Process Automation (RPA)
Agentic AI is transforming RPA from a rule-following script into a dynamic co-pilot for digital workflows. Traditional RPA tools are great at automating repetitive tasks like invoice entry, or data migration. But they break when logic changes or edge cases arise.
Agentic systems can:
- Interpret vague task objectives (e.g., “reconcile this vendor account”)
- Chain together multiple tools (CRM, Excel, email) to complete a workflow
- Handle exceptions by adapting or escalating based on context
But this flexibility creates new risks. If an agent can self-modify its workflow or execute across multiple APIs, it can also make unapproved financial decisions, trigger system calls, or access sensitive data if not sandboxed properly.
Customer Service
In customer service environments, agentic AI powers next-generation virtual agents that go far beyond scripted chatbots. These agents can:
- Track ongoing customer issues over time
- Escalate to human support when stuck
- Query databases or order systems to take actions (e.g., issue refunds, reroute deliveries)
This creates a more human-like experience. But it also leads to a more complex threat model. A manipulated or misconfigured agent might issue unauthorized refunds, leak PII through dynamic responses, or be used as a vector for lateral movement across internal systems.
These agents need strict output filtering, fine-grained access control (like CBAC), and continuous monitoring to detect behavior drift or prompt manipulation.
Healthcare
Agentic AI has powerful implications for healthcare operations and clinical decision support. Use cases already emerging include:
- Care coordination agents that schedule follow-ups, route test results, and communicate between teams.
- Medical coding assistants that extract diagnosis codes from patient records and submit claims.
- Triage bots that collect symptoms, assess risk levels, and direct patients accordingly.
However, because these agents operate in environments where data sensitivity is extremely high and decisions may be life-critical, the margin for error is razor-thin.
Organizations must pair agentic innovation with hardened architectures: encrypted data flows, red teaming of agent behaviors, and immutable audit trails to trace every decision the system makes.
Benefits of Agentic AI
Agentic AI introduces a new operational paradigm in which intelligent agents take initiative, adapt to changing goals, and continuously optimize workflows. When deployed responsibly, this capability delivers tangible enterprise value across several dimensions:
Enhanced Efficiency
Agentic systems can autonomously complete complex workflows that would normally require human coordination across tools or departments. This reduces manual effort and shortens cycle times in domains like RPA, sales ops, and DevSecOps.
Improved Decision-Making
By combining goal-oriented reasoning with contextual memory, agents can synthesize data, evaluate options, and adjust course dynamically—making them ideal for decision support in environments like healthcare, logistics, or finance.
Scalability
Agentic AI can manage multiple goals or users in parallel, expanding operational capacity without needing proportional headcount increases. This is especially impactful in areas like customer service or internal copilots.
Innovation Acceleration
With the ability to prototype, test, and iterate autonomously, agentic AI lowers the barrier to experimentation. It helps teams explore new product ideas, optimize business processes, and uncover efficiencies faster than traditional methods.
Agentic AI Security and Compliance Considerations
While traditional GenAI threats like prompt injection and data leakage still apply, agentic AI introduces stateful, context-aware risks that evolve over time. Below is a breakdown of the top security concerns specific to agentic systems, and the proactive defenses enterprises must begin integrating into their Generative AI security standards.
How to Get Started (Securely) With Agentic AI
Establish Observability from the Start
Autonomous systems without visibility are black boxes waiting to go rogue. Implement:
- Prompt logging: Capture every system + user input
- Execution traces: Map the agent’s planning and action loop
- Memory lineage: Record what facts were remembered, and why
- Tool audit trails: Track every external call, with timestamps and payloads
Consider LLM-aware observability platforms or route traffic through a secure gateway like Lasso to centralize logs and control flows.
Implement Guardrails with Code, Not Just Prompts
Relying on system prompts for policy enforcement is fragile. Instead:
- Use middleware or gateways to enforce identity, rate limits, and API boundaries
- Apply goal-consistency checks to detect intent hijacking
- Deploy role-based access control (RBAC) at the tool level, not just at the model interface
If you’re connecting to production data, use scoped API keys, isolation layers, and test in a non-deterministic sandbox first.
Test Like a Read Team, Not Just a QA Team
Agentic AI is dynamic and non-deterministic. Traditional unit testing won’t catch:
- Goal misalignment over time
- Toolchain abuse scenarios
- Memory poisoning or leakage
- Emergent behavior under edge-case prompts
Set up adversarial testing loops. Use shadow agents to simulate threats. Don’t assume "it won’t happen”. Simulate: what if it does?
Lasso’s Role in Securing Agentic AI
As we speak, AI-powered agents are acting with autonomy, and reasoning across sessions. Somewhere, an AI agent is even orchestrating complex tools on behalf of users. That means enterprises need to move beyond reactive security and adopt architecture-level controls designed specifically for this new paradigm.
At Lasso, we’ve built our platform from the ground up to meet this moment.
Built for Autonomy, Not Just Outputs
While many GenAI security tools focus on prompt filtering or API gateways, Lasso's approach is fundamentally different. We recognize that in agentic environments, the attack surface extends to every layer of behavior: goal-setting, memory, tool usage, and privilege handling.
That’s why Lasso secures the full agent lifecycle with:
- MCP Gateway for dynamic context enforcement, memory isolation, and prompt traceability.
- Deputes, our agent-native enforcement engine, which governs identity, tool access, and permissions in real time.
- Immutable logging and cryptographic traceability for every decision, input, and execution step.
- Autonomous policy enforcement that adapts to changing risk, not just static rule sets.
This approach is already playing an important role in shaping industry conversations around agentic AI, like our recent appearance at RSAC 2025.
Secure the Mission, Not Just the Model
Whether you’re building internal co-pilots, automating critical workflows, or deploying multi-agent ecosystems, Lasso ensures your GenAI stack is protected end-to-end. From avoiding memory poisoning to stopping tool misuse and privilege escalation, our platform applies proactive, context-aware defenses that evolve with your agents.
The era of agentic AI is already here. With Lasso, you’re building secure autonomy into the foundation of your AI strategy.
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