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What is agentic AI?

Learn how agentic AI reasons, plans, and takes action—transforming modern software development.
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Agentic AI defined

At its core, agentic AI refers to AI systems that have agency: the ability to make decisions and take actions independently in pursuit of a goal.
Unlike traditional AI models or chat-based assistants, agentic systems:

  • Perceive their environment by ingesting data, signals, or user input
  • Reason about that information to create a plan 
  • Act by calling tools, APIs, or systems
  • Reflect on outcomes and adjust their approach

This continuous loop allows agentic AI to operate dynamically—handling multi-step tasks, recovering from errors, and adapting to changing conditions without constant re-prompting. The result is software that can move work forward on its own, while humans stay focused on direction and outcomes.

  • Focused on action and agency: Generative AI excels at creating text, images, or code. Agentic AI builds on that foundation by deciding what to do next and executing against a goal.
  • Reasoning plus execution: Large language models (LLMs) provide the reasoning engine, while agents add tools, memory, and autonomy to interact with real systems.
  • Designed to adapt: Agentic AI can revise plans mid-execution, which makes it better suited for real-world, unpredictable workflows.
  • Different oversight model: Teams supervise outcomes rather than guiding every step, enabling systems to scale without constant human input.

From prompts to plans to actions across your stack—how agentic AI works

Unlike generative or applied AI alone, agentic AI blends reasoning and execution to plan workflows, choose tools, act across systems, and adjust until the goal is met.

Agentic AI represents the next evolution of artificial intelligence—systems designed to act with purpose. Instead of waiting for a prompt and returning a static answer, agentic AI systems can perceive what’s happening, reason about the best next step, and take action to achieve a defined goal.For software development companies, this shift is foundational. Agentic AI moves AI from a productivity assistant to an autonomous collaborator—capable of planning, executing, and adapting complex workflows with minimal human intervention. Microsoft is investing heavily in this model as part of its broader platform for building intelligent applications.

Agentic AI vs. generative and applied AI
Agentic AI is often discussed alongside generative AI and applied AI, but each serves a distinct role.

  • Generative AI models, such as LLMS, are designed to create content. They can write code, summarize documents, or answer questions when prompted. Their role typically ends once an output is produced.
  • Applied AI systems are optimized for specific tasks, such as fraud detection or recommendation engines. They operate within fixed boundaries and follow predefined workflows.
  • Agentic AI combines the reasoning strengths of generative AI with the execution capabilities of applied systems. This allows agents to:
    • Plan multi-step workflows
    • Select and use tools autonomously
    • Adjust actions based on intermediate results
    • Operate across systems and data sources

Instead of providing guidance alone, agentic AI carries work through to completion.

The architecture of autonomous agents

Agentic AI systems are built around a repeating execution loop that closely mirrors human problem-solving.

  1. Perception
    Agents ingest inputs from users, applications, logs, APIs, or sensors. This context anchors decision-making in real-world signals.
  2. Reasoning
    Using an LLM or reasoning model, the agent evaluates the current state, defines sub-goals, and determines the next action to take.
  3. Action
    Agents carry out tasks by invoking tools—running code, querying databases, calling APIs, or triggering workflows.
  4. Reflection
    After acting, the agent evaluates the outcome and refines its approach before continuing.
Supporting this loop are key technical components, including long-term memory (often powered by vector databases), function calling to translate intent into executable actions, and feedback loops that support continuous improvement.

Understanding agent-based models

Agent-based models are the building blocks of agentic AI systems. Each agent is designed with a specific role, goal, and set of constraints.
Rather than relying on a single, general-purpose assistant, development teams define specialized digital personas, such as:

  • A security analyst agent
  • A code generation agent 
  • A testing or validation agent

These models make it easier to simulate complex systems, test scenarios, and scale AI-driven workflows across the enterprise. Many of these patterns are already being applied across Microsoft’s broader ecosystem for software development companies.

The power of multi-agent systems

In multi-agent systems, multiple specialized agents collaborate to solve problems that are too complex for a single agent to manage effectively.
This approach mirrors how strong engineering teams work:

  • One agent generates code.
  • Another reviews it for quality.
  • A third checks security or performance considerations.

Benefits of multi-agent systems include improved accuracy through cross-verification, reduced errors through peer review, and modular designs that allow teams to evolve individual agents without reworking the entire system.

The role of AI orchestration

As agentic systems grow more complex, AI orchestration becomes a critical layer.
Orchestration governs how agents interact, including:

  • Communication patterns between agents
  • Decision authority and escalation paths
  • Conflict resolution
  • Alignment with business rules and safety requirements

Common orchestration approaches include hierarchical models, where a manager agent delegates work, and collaborative models, where agents coordinate as peers. Strong orchestration helps organizations deploy agentic AI with confidence and predictability.

Agentic AI in software development

For software development companies, agentic AI introduces new ways to accelerate delivery and reduce operational overhead.
Intelligent development workflows
Agentic systems can plan features, generate code, run tests, and refine implementations as requirements evolve. These capabilities align closely with Microsoft’s guidance for building modern, AI-powered development practices (https://www.microsoft.com/en-us/software-development-companies/develop).
Agentic testing
Autonomous testing agents can explore edge cases, generate test scenarios, and validate fixes with minimal manual input.
Legacy modernization
Agentic AI can analyze legacy systems, map migration paths, generate updated code, and validate compatibility—shortening timelines for modernization initiatives.
Reduced developer toil
By automating repetitive tasks, agentic AI allows developers to spend more time on architecture, design, and innovation.

Why agentic AI matters to you now

As systems become more distributed and software environments more complex, static automation struggles to keep pace. Agentic AI offers a practical way forward by enabling software that can reason, act, and adapt across changing conditions.
If your organization is looking to build the next generation of intelligent applications—or looking to strengthen your position within the Microsoft partner ecosystem through programs such as ISV Success—agentic AI provides a foundation for scalable, resilient innovation.

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FAQ

Frequently asked questions

  • Traditional AI systems respond to inputs. Agentic AI systems make decisions, take action, and adapt as conditions change in pursuit of a goal.
  • Applied AI focuses on specific, well-defined tasks. Agentic AI is designed for workflows that evolve over time and require planning, execution, and adjustment.
  • Examples include autonomous testing agents, self-healing infrastructure, and multi-agent development systems that plan, execute, and review work.
  • Not quite. LLMs provide reasoning capabilities, while agentic AI systems also include tools, memory, orchestration, and execution layers.
  • Organizations use agentic AI to automate development workflows, modernize legacy systems, and reduce operational effort across engineering teams.
  • Tools include APIs, databases, code execution environments, and workflows that agents use to interact with real systems.
  • Teams usually start with a focused use case, such as testing automation, code review, or internal tooling. From there, they introduce agents with human oversight, add orchestration and guardrails, and scale towards more autonomous, multi-agent systems.