More and more software companies are introducing “AI Agents”.

I’m sure you’ve seen Salesforce’s Agentforce marketing everywhere, just like we have. and we think that's just the beginning. If 2024 was the year of ChatGPT, generative AI, and LLM-powered software getting wide adoption, 2025 may be the Year of AI Agents – especially in SaaS.

We think of AI Agents like a teammate – with deep functional experience – who follows verbal instructions to execute an end-to-end workflow with minimal human intervention.

This article breaks down everything you need to know about AI Agents and how they might change the infosec role. 

What is Agentic AI?

Agentic AI is an artificial intelligence system that serves as the technology behind AI Agents. It is what enables them to operate autonomously without constant human oversight. You can think of agentic AI as the “critical thinking brain” that allows AI agents to think, learn, and act on their own to complete complex tasks from end to end.

Using Agentic AI helps teams build AI Agents faster and make the overall process much easier. It simplifies the way companies can customize AI agents to meet their specific use cases whether those needs are in sales, customer service, or other departments like infosec and compliance.

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What is an AI Agent?

AI agents are autonomous software programs that can independently perceive their environment, make decisions and take independent actions to achieve specific goals without needing human intervention. They combine machine learning, natural language processing, and reasoning to:

  1. Process and understand complex inputs
  2. Make independent decisions based on the context and goals that are set
  3. Execute actions across systems and applications
  4. Learn from experience, improve, and provide feedback
  5. Collaborate with humans and other AI agents

The difference between AI Agents and traditional AI is that you would have to program traditional AI to execute each task. Agents, on the other hand, can adapt to new situations and edge cases and complete complex tasks without constant human oversight or input from users.

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Key capabilities of AI Agents

Autonomous decision-making

AI agents can make independent choices without human input by analyzing available data and following the rules and guardrails set. Like an experienced team member, they can evaluate situations, weigh the options, and choose the appropriate actions based on their understanding of your goals and the constraints you’ve set. For example, an agent might automatically prioritize a customer support ticket, decide when additional information is needed, collect that information, or escalate an issue if needed.

Continuous learning and adaptation

Agents learn from every interaction and outcome of the task they’ve completed and they work to improve over time. They will independently analyze what works, what doesn’t and adjust their approach accordingly. Just like how a new employee gets better at their job through experience, AI agents can learn from hundreds of interactions simultaneously and consistently apply the learnings to future tasks.

Multi-step task completion

Gone are the days where AI automates only individual tasks. These were the “before” times where we had AI workflows or just plain automation using software. Now, AI Agents can handle complex processes that require multiple steps, judgment, and decision-making without human intervention. For example, if you gave an AI agent an email marketing workflow, it could determine the type of content needed based on a specific trigger, write the email copy, determine the appropriate recipients, and send the email, all without you needing to guide it.

Understanding natural language

AI agents can understand and respond to human communication in everyday, natural language without requiring a specific format or command. They can understand context, intent, and nuance in conversations with your team or your customers so users can simply explain what they need in their own words, and the agent will understand and respond appropriately.

Integration with existing systems

AI agents act like a bridge between different systems, accessing and using information from a variety of sources to complete different tasks. This is key to reducing a lot of the human oversight required. This means these agents can work within your existing infrastructure by pulling data from your CRM for example, update the CRM or ticketing system as it works through a tasks, tap a collaborator through an internal chat system, and more. 

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Benefits of AI Agents

Set it and forget it - less manual work for teams

AI Agents, once configured, can independently manage workflows, process requests, and execute actions – all within guardrails you set, without requiring constant oversight. This frees up the team’s time spent on both repetitive tasks and also administrative project management type work such as getting the right people to collaborate with you, finding the right answer, or other back and forth communication you need to do with other teams or customers.

Better customers experience because of 24/7 availability

Because AI is well, AI and always available, customers can get immediate assistance if the AI agent is customer facing. They can handle multiple inquiries simultaneously, eliminating wait times and ensuring a customer gets what they need immediately. If it isn’t customer-facing, it can still provide faster results for teams internally that can then be given back to the customer. 

Consistency and accuracy 

AI agents deliver consistent performance across all interactions and can follow predefined parameters precisely, eliminating human error and ensuring compliance with established procedures. Humans who take on a task may vary in knowledge, experience, or training, but AI agents maintain the same standard of service quality across every interaction. They are also learning from each interaction, continuously improving how they assess and complete each task.

AI Agents in Information Security

Monitoring and detection

We think AI agents will transform information security because it can excel at things like compliance monitoring, reporting, and taking immediate action based on security policies. In addition, being able to automatically detect threats because they are “on” 24/7 and investigate in real time means that security professionals can focus on strategic work instead of monitoring type tasks.

Proactive security

Another significant change could be in more “proactive” security measures as AI agents can analyze patterns, predict potential vulnerabilities and do things like recommend security improvements based on its knowledge of your systems. They will be able to learn from each security event to improve detection and response capabilities over time.

Security reviews

One last workflow that many information security professionals also have to manage is completing security reviews for B2B customers. There is a lot of software on the market to automate tasks like sharing a SOC 2 behind an NDA and providing other security documentation as well as answering security questionnaires using AI, but AI agents will go beyond just these activities to drastically reduce the human effort by managing the entire workflow end to end.

It can do things like process requests from sales based on the guardrails you provide, determine if the customer needs a link to the trust center or a questionnaire answered, and process and answer the questionnaire based on deal thresholds. It will also notify subject matter experts and other team members when it needs them to provide additional information or review and answer and all of this is done autonomously. A infosec team member managing this process wouldn’t even see the request to complete a security questionnaire, for example, until a first draft was done and the AI agent notified them to come in and review where needed.

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How to get started using AI Agents

Identify the tasks that need to be automated

Rank the workflows your team manages by the ones that take the most manual effort (FTE hours per week). You can also assign a rank (based on hours, or how complex a task is)  to the activities within each workflow to determine which are the best candidates to automate.

Look for repetitive work that follows clear patterns or rules - these are perfect for AI agents.

They can also handle sourcing from many large data sets and systems so don't be afraid to assign tasks that are tedious to handle because of how many data sources humans might need to search through.

Choose the right platform

Many software providers are moving in the direction of AI agents with pre-built agents for you. You can start there and consider factors like ease of use, integration with your existing systems and tools, the maturity of their product, cost, and support available. You will want to make sure it can handle the specific tasks you’ve identified to automate and works with your current systems.

Prepare your data

You’ll need to organize the data the agent needs to do its job. Are there rules, policies, procedures you want it to follow? Or do you want it to look at historical data? You’ll have to figure out where all of this data is and make sure the platform can connect to these or ingest these sources with ease.

Start small, train and test

Typically, you can start with a single, well-defined task rather than trying to automate everything at once. If you pick something important, but lower risk as a test, you can learn how the platform or system works, how it handles data, and build from there. For example, Conveyor’s AI agent for Customer Trust can handle triaging requests from sales so you might have it start by looking at Salesforce cases or Jira tickets to see if it can properly handle the data in each request, or you might have it just do the security questionnaire answering piece to see if the agent can provide answers based on the rules you set for tone, length of answers, and more and notify the right people when it needs help to complete the first draft.

Monitor, adjust and scale

You’ll be doing a lot of monitoring at the beginning to see how it performs. You’ll have to check its accuracy, review its decisions and gather user feedback on performance. You can use this information to adjust its training and the rule set you gave it. AI agents can generate valuable data on its interactions with your team or customers so you can make strategic decisions and adjust policies and workflows using data driven insights. It’s worth noting that working with a platform that has data on how their AI performs in terms of accuracy is a plus, and can give you more confidence in how effective their AI agent will be.

Gradually expand to other areas after you’ve learned from and adjusted for some of those first tasks. You’ll use what you learned from your initial implementation to figure out how you want to expand.

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What's next?

Ready to try an AI agent?

Conveyor launched the first AI Agent for Customer Trust. You can go beyond just sharing NDA-gated documents (like SOC 2) with customers or answering security questionnaires when the AI agent will execute every communications or coordination task in between. 

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