Agentic AI is: One of the most talked-about subjects in relation to artificial intelligence – and for good reason. It represents a shift from AI systems that simply respond to prompts, into systems that can act, taking initiatives and carrying out multi-step tasks.
Agent Runtime
While the benefits of employing agents can be incredibly positive, there are still concerns about AI agents “going rogue” or accessing tools and data which they should not be using. Unfortunately, this truly is a real concern as agents are non-deterministic (can make unexpected decisions or hallucinations) or they may be led to access inappropriate data through malicious prompt injections. The good news is – there are ways to control an agent’s actions to prevent these scenarios. Agents can (and should) be managed by an Agent runtime infrastructure which validates an agent’s actions, allowing or blocking the action, before each task execution.
To better understand this process, let’s take a look at how a runtime infrastructure might deal with a potential prompt injection. Malicious Prompt injections occur when an attacker injects malicious instructions into an agent’s workflow execution. For example, an AI agent may be triggered to perform a task by an email which contains instructions. An attacker could send a similar email which contains slightly altered instructions to retrieve sensitive data which is not related to the task. Agent runtimes inspect the requested actions, checking that instruction commands and data requests meet the defined parameters for the workflow. If the runtime finds that the request does not fall within the defined parameters, the runtime blocks the request to prevent unauthorized or unsafe actions.
Besides active control of establishing security and guardrails, Agent runtime infrastructures also handle
- Tool selection (which tool to use and when)
- Structured tool calls (e.g., JSON arguments, APIs)
- Error handling (timeouts, failed requests, retries)
- Memory (saving useful context)
- Permissions (what the agent is allowed to do)
- Logging and tracing (so humans can audit decisions)
- Computing Resource allocation (scaling up or down based on task complexity)
- Isolation (Sandboxing – preventing Agents from updating production environments)
Orchestration Layer
When complex processes require a workflow to have multiple steps, an Orchestration Layer helps direct the traffic of a workflow. While the runtime controls and executes the direct actions of an AI agent, the orchestration layer directs the overall flow of the actions. Output from one step of the workflow is passed to the next step so that workflow sequencing can remain intact and the agent receives all the information it needs to prepare for the next step.
Prompt Wrapper
On a more detailed level, prompt wrappers are used as an extra level of containment and security to ensure that agents are controlled. A prompt wrapper can receive data input and structure the agent request to produce a more consistent and reliable output. For example, the wrapper can include directions for which files to use, which data points to pull and the format in which to return the data. Also, prompt wrappers are written in the language which the runtime can easily consume.
Prompt wrappers are an essential part of the agent workflow. Learn more about wrappers in our blog “The Importance of using an AI Wrapper”.
Human in the Loop
Even with all the benefits of having technology governance in place to control the actions of agents and prevent exposure to attacks, the fact remains that a human should always be present as part of the workflow to review end results. Employees who know your business are the best resource for reviewing agent output to ensure the results are correct.
Having humans in the loop doesn’t mean that productivity gains are lost. The work is still automated and accomplished through agents, but the end verification is undertaken by employees – greatly limiting the amount of time they must dedicate to the process.
Spheregen offers a range of solutions that utilize Agentic AI and Automation systems. If you are interested in learning more or have questions about how Agentic AI can assist in your workflows. Contact us today and we can explore various solutions together.
FAQs
What Forward-Thinking Teams Ask About Ai
Implementing Intelligent Automation raises important questions. To help, we’ve put together answers to the most common questions we encounter. This will give you a better sense of how IA works, what to anticipate and how SphereGen can kickstart and support your Intelligent Automation journey.
Many Intelligent Automation initiatives deliver value in weeks—not months. Organizations often see early wins through targeted use cases before expanding across the enterprise.
Absolutely. Intelligent Automation is designed to work seamlessly with your existing technology stack, including ERPs, CRMs, legacy platforms, and cloud applications.
Yes. Enterprise-grade Intelligent Automation solutions include built-in security, governance, and compliance controls—ensuring transparency, auditability, and trust.
No. Intelligent Automation enhances human capabilities. By automating repetitive work, teams gain time to focus on innovation, customer engagement, and higher-value decision-making.
It starts with identifying the right opportunities. A focused assessment helps prioritize high-impact processes and build a clear roadmap for success.
Any process that is repetitive, data-heavy, or decision-driven is a strong candidate. Common use cases include document processing, customer service workflows, compliance checks, and reporting.
Intelligent Automation drives measurable impact—lower operating costs, faster turnaround times, higher accuracy, and better customer experiences—while enabling teams to focus on strategic work.
Traditional automation follows fixed rules. Intelligent Automation adapts. It uses AI to handle complexity, learn from patterns, and respond intelligently to real-world scenarios.