Agentic AI refers to artificial intelligence systems designed as agents—entities capable of autonomous action, decision-making, and interaction with their environment. Unlike traditional, rule-based automation or most LLM-powered chatbots, agentic AI can plan, reason, and execute multi-step workflows, adapting as it goes.
McKinsey defines agentic AI as “a system based on generative AI foundation models that can act in the real world and execute multistep processes” (McKinsey).
The University of Cincinnati, citing Microsoft, describes it as “an autonomous AI system that plans, reasons and acts to complete tasks with minimal human oversight” (UC).
What Are AI Agents?
AI agents are software entities that:
- Observe their environment (via application programming interfaces, sensors, or data feeds)
- Set goals and plan actions
- Act, monitor progress, and adapt based on feedback and user behavior
- Collaborate with other agents or human agents when needed
These intelligent agents often blend large language models (for flexible reasoning and natural language processing) with deterministic programming (for speed and reliability), providing both adaptability and stability (McKinsey).
How Agentic AI Works
Agentic AI operates through a cycle of perceiving, reasoning, acting, and learning:
- Perceive: Autonomous AI agents collect and process real-time data from external tools, APIs, sensors, and user interactions to stay informed of the environment and process data effectively.
- Reason: Using large language models and machine learning algorithms, agents analyze data, interpret context, and develop plans to perform tasks and solve complex problems.
- Act: Agents execute tasks autonomously by interacting with external systems, software development platforms, or workflows, performing complex processes without constant human intervention.
- Learn: Through reinforcement learning and continuous feedback loops, AI agents learn from outcomes and improve their decision-making and efficiency over time.
This process enables AI agents to automate complex tasks, optimize supply chain management, enhance customer relationships, and serve customers across diverse job functions.
Why It Matters: A Shift in Automation
Legacy automation is powerful for repetitive tasks, but it struggles with ambiguity, change, or orchestration across silos. Agentic AI addresses this by offering:
- Autonomy: Agents act independently, reducing constant oversight and minimizing human intervention (UC).
- Adaptivity: Agents refine strategies in real time as conditions change, adapting to dynamic environments (Logic20/20).
- Collaboration: Multi-agent systems coordinate with human teams or other agents across domains (McKinsey).
Market analysts forecast rapid adoption—Gartner estimates one-third of enterprise software will embed agentic AI by 2028 (Reuters).
Step-by-Step Implementation
Up to this point, we’ve looked at agentic AI in broad, cross-industry terms. From here, we’ll narrow in on solutions that matter specifically for platform engineers. For example, kagent is an agentic AI framework for Kubernetes that helps DevOps/platform engineers to build and run AI agents in k8s. It provides a foundation for building AI-driven solutions in cloud-native environments. agentgateway, on the other hand, has broader applicability as an infrastructural proxy for observability, security, and routing across any agentic framework or environment.
- Architect Your Agentic System
Identify workflows where autonomy or adaptive reasoning adds value (e.g., infrastructure remediation, IT support, data aggregation). - Choose a Platform or Framework
Enterprise platforms (e.g., orchestration layers like Solo.io’s kagent) focused on Kubernetes platform operations or infrastructural components such as agentgateway (which provides observability, security, and routing) help establish guardrails and integrations. - Build and Register Agents
Define agent capabilities and implement or register MCP servers your agent will use. - Enable Secure Communication
Use service meshes (e.g., Ambient Mesh) for encryption, policy enforcement, and zero-trust networking between agents and tools. - Monitor, Iterate, and Scale
Track agent and MCP servers’ behavior and outcomes with observability tools, update agent behavior and test different models, and expand gradually.
Benchmarks / Case Studies
- Enterprise Automation: McKinsey highlights organizations using agentic frameworks to cut manual tickets by up to 60% (McKinsey).
- Service Mesh at Scale: Solo.io’s Ambient Mesh demonstrates secure, high-availability environments in large Istio deployments. Without a service mesh, enforcing zero-trust uniformly is nearly impossible—manual integration across dozens or hundreds of services quickly becomes brittle and error-prone. A mesh automates and standardizes encryption, identity, and policy controls, making zero trust both practical and achievable at scale.
- Composable Agents: kagent enterprise improves developer agility by allowing teams to compose workflows from both built-in and bring-your-own agents. Whether you’ve already developed an agent using ADK, LangChain, or another framework, or you want to define new agents declaratively, kagent provides the infrastructure to register, orchestrate, and run them alongside cloud-native or legacy systems.
Advantages of Agentic AI
Agentic AI offers several key benefits over traditional AI and automation:
- Autonomy: AI agents operate independently, reducing the need for constant human oversight and enabling faster decision-making.
- Adaptability: Continuous learning allows agents to adjust strategies dynamically as conditions change.
- Efficiency: By automating complex workflows and repetitive tasks, agentic AI boosts employee productivity and optimizes business processes.
- Scalability: Multi-agent systems can coordinate to handle large-scale operations across multiple systems and job functions.
- Enhanced Customer Engagement: AI-powered agents improve customer service inquiries and customer interactions by providing personalized, timely responses.
Challenges of Agentic AI
Despite its advantages, agentic AI presents challenges:
- Risk of Unintended Actions: Autonomous agents may act unpredictably if not properly guided by human intent and guardrails.
- Complex Orchestration: Managing multi-agent interactions across diverse systems can introduce bottlenecks and failures.
- Security Concerns: Autonomous operation requires robust security measures to protect sensitive data and prevent breaches.
- Data Privacy: Handling patient data, market data, and customer information necessitates strict compliance with privacy regulations.
- Implementation Complexity: Implementing agentic AI within existing infrastructure and workflows demands significant expertise and resources.
Pitfalls & Avoidance Tips
- Over-automation Risk: Not every process should be fully autonomous—keep a human-in-the-loop for high-risk scenarios.
- Orchestration Complexity: Without infrastructural components like agentgateway (which provides observability, security, and routing), agent interactions can become brittle and opaque.
- Security Blind Spots: Skipping zero-trust or mTLS exposes systems to lateral movement or data leaks (IBM).
- Observability Gaps: Without monitoring, debugging agents becomes difficult; adopt tracing and analytics early.
Advanced Tips
- Multi-Agent Collaboration: Use orchestration features to compose complex scenarios that involve multiple agents and MCP tools.
- Hybrid / Multi-Cloud Deployments: Service meshes enforce consistent policies across environments.
- Bring Your Own Agents: Incorporate agents you’ve already built with ADK, LangChain, or other frameworks into kagent enterprise, running them alongside declaratively defined agents for maximum flexibility.
- Continuous Skills Improvement: Hands-on labs (e.g., kagent Labs) build expertise as the field evolves.
FAQ
How is agentic AI different from automation or chatbots?
It doesn’t just respond—it plans, adapts, and coordinates across systems (UC).
Why do I need a service mesh?
Agents and MCP tools are a type of workload where a mesh like Ambient provides encryption, observability, and policy enforcement.
What industries benefit most?
IT, finance, healthcare, and manufacturing—any sector with complex cross-system workflows (Logic20/20).
Conclusion
Agentic AI marks a fundamental leap in enterprise automation. By combining autonomous planning, adaptive reasoning, and secure execution, it enables software agents to manage, optimize, and even reinvent workflows.
To go deeper, explore McKinsey’s agentic AI research, IBM’s perspectives, or Solo.io’s kagent for enterprise-ready implementation paths.
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