Agentic platforms are gaining attention, but there’s little clarity on what they actually are and why they matter. At their core, agentic platforms transform how organizations operate by empowering artificial intelligence agents to perform complex, autonomous tasks. These platforms don’t just automate repetitive tasks; they prioritize organizational outcomes, reducing the need for human intervention. This article describes what an agentic platform is and outlines its essential capabilities in building autonomous agents to improve your organizational efficiency and adaptability.
Introduction to Agentic AI
Agentic AI enables systems to operate autonomously with minimal human intervention. Agentic AI systems are purpose-built to tackle complex challenges and automate intricate business workflows in all areas of your business. By leveraging advanced natural language processing and machine learning, agentic platforms can analyze data, infer customer intent, and make decisions that align with organizational objectives. This autonomy allows human workers to focus on higher-value tasks, which drives operational efficiency and fosters innovation.
What is an Agentic AI System?
An agentic platform is designed to create autonomous AI agents—intelligent, software-driven tools that execute tasks traditionally handled by people. Unlike other tools focused on individual tasks, agentic platforms take an organization-wide view. They automate actions, identify outcomes, and orchestrate entire workflows to support larger goals rather than single tasks.
At Krista, we’ve pioneered agentic platforms with a clear focus on business outcomes. Krista agents orchestrate outcomes and perform actions, whether triggered by an external email, a system alert, or an internal request. These agents are capable of autonomously operating workflows with minimal human oversight, emphasizing their decision-making, planning, and adaptability. They use artificial intelligence to interpret the task, integrate with various systems, and then execute the action, producing valuable results autonomously. This ensures the outputs are outcome-oriented, aimed at delivering real organizational value by automating complex workflows and not just making employees more effective.
How Agentic AI Works
An agentic platform integrates advanced technologies to drive meaningful outcomes. Agentic agents interact with users to determine when to answer a question, prompt for clarification, or engage in a conversation. This flexibility enables smooth, intuitive communication between users and the platform.
Agentic AI leverages multiple types of machine learning to make accurate decisions and predictions. The platform includes categorizers, predictors, anomaly detection, and natural language processing (NLP) to dynamically select appropriate ML models as data evolves, optimizing accuracy and efficiency. When confidence levels are low, agentic platforms escalate exceptions to people, ensuring human oversight for complex scenarios and critical actions.
Agentic platforms incorporate retrieval-augmented generation (RAG) from company documents and enterprise systems, enabling agents to respond with context-specific knowledge. Real-time data access enhances RAG, providing up-to-date information and a reliable foundation for generative AI prompts that align with the organization’s unique context and needs.
Built for adaptability, agentic platforms allow organizations to switch AI providers as technology advances, ensuring top performance while managing costs. Finally, these platforms seamlessly integrate human and machine collaboration to achieve strategic outcomes rather than focusing on isolated tasks. This comprehensive approach ensures agentic AI delivers high-value results that are effective, flexible, and tailored to organizational goals.
Key Features of Agentic AI:
- Dynamic Interaction: Detects when to answer questions, request clarifications, or engage in conversation using NLP.
- Machine Learning Capabilities: Utilizes categorizers, predictors, anomaly detection, and NLP to select and optimize ML models dynamically.
- Human Oversight: Escalates low-confidence scenarios to people, ensuring accurate decisions in complex cases.
- Context-Specific Knowledge: Integrates RAG with company documents and systems to provide precise, real-time answers tailored to organizational needs.
- Generative AI Integration: Supports generative AI prompts with reliable, real-time data and context.
- Adaptability: Allows switching AI providers to stay current with advancements and manage costs.
- Human-Machine Collaboration: Enables workflows that achieve strategic outcomes, combining human and AI strengths effectively.
Key Benefits of Agentic AI
Agentic AI offers a multitude of benefits that can transform organizational operations. Here are some of the key advantages:
Essential Capabilities of an Agentic Platform
Produces Autonomous AI Agents
At the heart of an agentic platform are autonomous AI agents designed to work independently with limited direct human supervision. These agents recognize when an outcome needs attention and handle it—whether it’s processing an incoming customer request, updating a record, initiating a workflow, or other complex challenges. They operate without constant human input, which enables organizations to move work from people to software so they focus on strategic priorities rather than micromanaging routine tasks. Autonomous agents streamline processes, allowing the organization to achieve more with less human intervention.
Data Integration and Analysis
One of the standout capabilities of agentic AI systems is their ability to integrate and analyze diverse data from existing enterprise systems. This comprehensive data integration provides a 360-degree view of an organization’s operations, enabling smarter, data-driven decisions. By tapping into a wealth of data, agentic AI can identify patterns, generate actionable insights, and automate complex workflows. This not only enhances decision-making but also ensures that actions taken are aligned with the organization’s strategic objectives.
Has Low-Code Configuration and Change Management Features
True agility requires rapid adaptability, and low-code configuration makes this possible. Low-code configuration enables the rapid development and adaptation of AI solutions, allowing autonomous agents to optimize their actions in complex environments. An effective agentic platform doesn’t need complex coding every time you need a change. Instead, it enables quick modifications without burdening development teams, reducing backlog and speeding up implementation.
With a low-code or ideally no-code approach, organizations can configure agents as conversations among people, systems, and AI using every day language. This capability allows companies to describe what they need the agent to do and implement changes rapidly—without waiting on traditional development cycles. In other words, low-code empowers businesses to instantly adapt and evolve their automated workflows quickly and efficiently.
API Integration Capabilities
Integration is critical. For an agentic platform to deliver value, it must connect with various systems and data sources. While many platforms label basic connections as “API integration,” true integration goes far beyond that. At Krista, we use natural language-based integration, where agents interpret and execute commands in familiar language rather than relying on rigid APIs. This flexibility allows even non-technical team members to leverage integrations without relying on developers.
This approach avoids the complex, code-heavy integration models of the past. With natural language, an agent can connect with systems across departments—whether it’s pulling customer data from CRM, updating records in HR, or interacting with third-party applications. This flexibility gives businesses a comprehensive integration solution without the need for constant coding.
Multi-Channel Engagement with Natural Language Processing
Multi-channel engagement means agents can interact across different communication platforms, whether it’s email, SMS, VoIP, Slack, or other channels. In a world where organizations operate across multiple communication tools, this adaptability is essential.
An agentic platform must engage users wherever they work, not limit them to a single communication channel. For example, an agent might receive customer interactions and requests through a chatbot on the website and follow up with internal stakeholders via email or Slack. This multi-channel engagement keeps workflows connected, responsive, and aligned with the organization’s communication style.
Can Escalate Complex Workflows to Humans with Human-in-the-Loop Capabilities
No matter how advanced, some tasks or exceptions will require human input. Human-in-the-loop capabilities ensure that agents can escalate tasks to people when needed, integrating human insight into automated workflows. This is crucial in scenarios where a decision, review, or approval must come from human oversight.
Intelligent agents must make this process smooth. When an agent escalates a task, it should hand off to the appropriate team member, track progress, and return to the workflow once completed. The agentic platform enables this collaboration, so workflows remain efficient and responsive, blending human and machine strengths.
Continuous Learning and Improvement
Agentic AI agents are designed to learn from their experiences and improve over time. Utilizing machine learning algorithms and reinforcement learning, these systems can adapt through trial and error, to refinine their performance. This continuous learning capability allows AI agents to become increasingly proficient at tackling complex challenges and automating workflows. As a result, human workers are freed from routine tasks, enabling them to focus on more strategic, high-value activities. This ongoing improvement ensures the agents remain effective and relevant in dynamic environments.
By integrating these new sections, we provide a comprehensive understanding of agentic AI, its workings, and its benefits, maintaining the informative and professional tone of the original article.
Enforces Security Guardrails
Security forms the backbone of any agentic platform. Unlike platforms that rely solely on standard access permissions like those in SharePoint or Salesforce, a true agentic platform enforces security holistically, with role-based access and context-specific controls. It implements attribute-level permissions to manage what each user can see and do.
For instance, when an agent shares a new employee announcement, it reveals only relevant details—like name and position—while restricting sensitive information, such as salary, to authorized individuals. By integrating generative AI, the agentic platform enhances security through role-based access and prevents sensitive data exposure by adapting access controls to changing contexts. This approach mitigates risks of unauthorized access and inaccurate outputs.
Build Your Autonomous Agents with Krista
Agentic AI promises a new era of automation, one that focuses on outcomes, not tasks. With these essential capabilities—producing autonomous agents, low-code configuration, seamless API integration, multi-channel engagement, human escalation, and robust security guardrails—an agentic platform drives real organizational value.
In a world where speed, efficiency, and security are paramount, agentic platforms offer a transformative solution. By automating complex tasks and integrating seamlessly with existing systems, they empower organizations to operate more effectively, adapt rapidly, and stay secure. For businesses ready to embrace this transformative wave of automation, agentic AI represents an opportunity to achieving top-tier productivity and innovation.
Links and Resources
Speakers
Transcription
Chris Kraus
John, there’s been a lot of hype lately about agentic platforms, but no one’s clearly explaining what they are and how they can help.
John Michelsen
An agentic platform is a term for something we’ve been focused on since founding in 2019. It’s designed to produce autonomous agents, which perform tasks that people typically handle, but instead, software executes these tasks.
The challenge for a company like ours, with extensive experience delivering this to many customers, is that an agentic platform should focus on outcomes for the organization, rather than how many seat licenses can be sold. Many software companies think about creating a virtual assistant for every employee, focusing on individual tasks. While an autonomous agent that accomplishes tasks is beneficial, we aim for agents that prioritize the organization’s perspective.
These agents autonomously identify outcomes that need execution, triggered externally, by an inbound email, or through a feed. They then carry out tasks via workflows, automate steps through seamless system integration, and apply various AI capabilities, achieving valuable outcomes with autonomy.
Chris Kraus
John, so what’s inside an agentic platform? Are there specific capabilities people need to know about?
John Michelsen
Yes, there are essential capabilities that enable it. First, an agentic platform is about producing autonomous agents. To create these effectively, there are some key requirements. One is low or no-code configuration. These agents need to be quick and easy to build and modify. System integration is crucial, and integration challenges must be solved for seamless functionality.
Another aspect is integrating people, as agents working towards outcomes will involve systems, people, and, of course, AI. This integration should happen across multiple channels, beyond traditional UIs, allowing for conversation in any channel we need. Finally, there are significant security concerns. We need robust, easy-to-implement guardrails to prevent exposure of sensitive data to unauthorized individuals. Security measures must be straightforward; otherwise, they risk being done incorrectly or not at all.
Chris Kraus
Let’s dive into this. When people talk about low-code configuration, I’ve noticed that some of these agents come with a forced template or sample where you can only add text. You can’t modify the workflow much, which might be why they call it low code—it’s limited in functionality and customization. What’s your take on that?
John Michelsen
Low-code configuration is indeed part of an agentic platform, but it’s important to define what “low code” means. I remember coding in assembly to make C code faster, so C was considered low code compared to assembly. But we need to change an automated agent’s behavior as quickly as we can describe the change we want. Using traditional tools with scripting languages like VBS or C# turns every adjustment into a coding change, triggering a software lifecycle, which creates backlog and slows us down.
An agentic platform requires not just low-code but ideally no-code configuration. We should be able to describe the outcome we want the agent to achieve as a conversation among people, systems, and AI. People are always part of the process, system capabilities are essential, and AI transforms it all. The entire configuration should be conversational, not based on outdated scripting languages.
Chris Kraus
What’s interesting is that many of these platforms label everything as “API integration,” but we know API integration is complex. You and I have been doing this since 2005, and some platforms just offer a few helpers, like connecting internal data in Salesforce or Microsoft SharePoint. But there’s much more to integrating data and context into a process. You can’t just say it’s low code and then require an API—a developer still needs to write it.
John Michelsen
You’re on the right track, Chris. We’ve both been working on this since 2005, and even before that. The transformation we’re aiming for in technology often gets bogged down in the integration layer. At Krista, we had to approach this very differently to achieve no-code configuration for autonomous agents.
Instead of relying on traditional API integration, or what’s commonly called an integration platform as a service, we think in terms of natural language, not APIs. Instead of figuring out how to use the technology, we make the technology adapt to us. Instead of creating an API layer for underlying systems where you’d need to write code, we apply a domain-specific taxonomy to a system. When we build a connector, it translates human language—nouns, verbs, and so on—into actions within the system. This creates a natural language layer that can be used by business users, unlike an API bus, which requires a development team. This approach will be a challenge for other platforms, as they’ll not only be slower to market but also slower to evolve compared to Krista.
Our ability to integrate so effectively is a key reason for our speed. Other platforms are still using outdated integration methods from 2018 or 2019 and will need to catch up.
Chris Kraus
Exactly. Many platforms rely on templates or examples. For instance, one template might work in SMS and Slack because it’s a chat template, while another is designed for reading emails. Microsoft’s Copilot, for example, is still all internal, with autonomous capabilities expected next quarter. But when it comes to multichannel engagement, it’s surprising that anyone would limit it to a single channel. If it’s an autonomous agent within an agentic platform, why restrict it to a single source?
John Michelsen
Chris, you have to recognize that a developer is coding to an API to implement low-code configuration for an autonomous agent. They have to choose either the SMS APIs, email APIs, or other specific APIs because they haven’t abstracted the outcome they’re aiming to achieve. In Krista, we can handle conversations through various channels—whether it’s a VoIP connection, email, text, or live voice—because we anticipated this need. Other platforms may catch up eventually, but we’ll have moved forward by then.
The key is to define outcomes independently of the channels through which people interact. For instance, you might receive an inbound message from a customer on a website chatbot and simultaneously communicate with leadership via email within the same outcome. Multi-channel capability within a single outcome is natural and essential. When you reach the maturity level we have, where agent outcomes are defined in a way that they can be delivered through any channel or a combination of channels, it places you in an excellent position. Other platforms, unfortunately, are still tied to API-based programming and scripting automation tools, which Krista has surpassed.
Chris Kraus
You’re right, John. We talk naturally about outcomes involving people, systems, and AI. We’ve discussed APIs and systems, but what about human escalation? It’s becoming more recognized as important in other tools, but will it be as complicated as coding a chat to interact with someone? What should it actually look like?
John Michelsen
This is an area that still puzzles me. Let’s go back to the BPM movement, which largely failed because it was too complex on the integration layer. However, the concept of human workflow and work queues was strong. Unfortunately, email became the default workflow tool, which is terrible for managing tasks. For every important request, you get flooded with spam and noise. Even collaboration platforms like Slack or Teams mark everything as read automatically without any task management structure.
A real work queue should confirm that an action is required, track that it’s performed, and validate it. Many platforms miss this, especially those trying to integrate humans naturally into workflows. An agentic platform must excel at human integration, with an elegant, outcome-oriented orchestration of multiple people and roles. Without role-based, rule-based escalations across multiple channels, platforms will struggle. Krista has mastered this, but others may face challenges as they try to adapt by using APIs from Slack, Teams, or email. They have a long way to go.
Chris Kraus
You mentioned role-based access, and it’s timely because I was going to bring up guardrails next. Many vendors talk about implementing guardrails just because analysts suggest it. They might say, “Our guardrail is SharePoint security, so you can only access specific documents,” or rely on Salesforce security layers. But real guardrails should encompass more, like preventing AI hallucinations. Can you discuss what guardrails should truly entail? I’m seeing vendors add bullet points, but they’re not addressing it holistically. Should it include AI guardrails, workflow guardrails, hallucination prevention, vendor lock-in prevention? There could be a hundred different guardrails. What should an agentic platform include?
John Michelsen
An agentic platform has to take a comprehensive approach to data and access security. One layer is ensuring that the underlying systems are properly integrated without creating administrator or super-user accounts, which would undermine all the security measures in place. This is often the first step for many platforms because it’s quicker to develop, but it creates vulnerabilities.
Beyond that, the platform needs to recognize who is permitted to see and do what, even down to specific attributes within a record. For example, if we send a new employee announcement company-wide, the system might pull the employee’s details from the HR system for the message. Only those with the right to see certain data, like salary or home address, should be able to view it. In this way, one message can go to everyone, but each person only sees the data they’re allowed to view.
Let’s say we need to change an employee’s salary. An agentic platform focused on outcomes will know who has permission to make that change. We can’t rely solely on systems of record for this, as they are just one part of the outcome. We’re likely crossing systems and roles within the organization, so we need a broader understanding of guardrails.
Simple file-sharing permissions in SharePoint, for example, won’t prevent misuse unless it’s tied to the specific outcome or use case. Only then can you ensure proper security. There’s much more to explore on this topic, but hopefully, I’ve clarified that system-of-record security is essential but insufficient to establish the guardrails needed.
Chris Kraus
John, at Krista, we’ve been delivering agentic platforms to customers in production since 2019. We discussed a definition at the beginning of the podcast and worked through it. Since we’ve been doing this actively with customers, what’s missing from their definitions? They don’t seem complete.
John Michelsen
There are a couple of major gaps. The biggest one is that these definitions are reactions to generative AI models, which everyone should be responding to. But an agentic platform that delivers sophisticated outcomes requires multiple AI capabilities—classification, anomaly detection, regression capabilities. That’s a huge missing piece. There will almost always be questions like “What is this?” or “Can you predict this number or date?” For true autonomy, agents must minimize human involvement, which requires a learning model.
Generative AI models are pre-built, and fine-tuning mostly involves adjusting weights rather than true learning. We need the ability to build AI on the fly for specific customer use cases. You can’t use another customer’s model or get one from a vendor; it has to be created for that unique scenario. We’ve recognized this need, built on it, and offered it in the market for years. For many customers, it’s an “aha” moment when they realize we can do this.
Another issue is velocity. Most platforms don’t offer the speed needed for real effectiveness. How often have you been in an IT project where you ended up using Excel because it’s faster? The speed of change with Excel is instant, while with IT-based systems, any change goes through a development cycle—dev environment, integration testing, etc. If you don’t have near-Excel speed for changes, these platforms will only handle minor tasks without moving the ball forward.
An autonomous agent that merely repackages a software company’s existing tools with generative AI still requires extensive scripting and a dev team. That approach won’t achieve the outcomes our agentic platform does.
