Skip to main content

Agentic AI Concepts Explained

Terms are the starting point for understanding a subject area.

1. Defining the AI Agent: The Autonomous Employee​

At its core, an AI Agent is like a skilled employee inside an organization.

A good employee:

  • Observes their work environment
  • Understands the current situation
  • Takes action to achieve a defined goal

Similarly, an AI agent is a software system that observes its environment, reasons about what's happening, and takes actions to achieve a goal.

The key difference from traditional automation is the same difference between a factory machine and a knowledge worker.

  • Traditional automation = a checklist worker who follows strict "if-this-then-that" instructions.
  • Agentic AI = a goal-driven employee who is told what outcome to achieve, not exactly how to achieve it.

If you tell an employee:

  • "Resolve this customer complaint"
  • "Reduce this month's cloud costs"

You don't provide every step. You expect them to:

  • Assess the situation
  • Decide what actions are needed
  • Adapt if something unexpected happens

That's what makes it agentic. It's not a linear script — it's a goal-directed contributor.

2. The Reasoning Engine: The "Brain" (How the Employee Thinks)​

An AI agent's reasoning engine is like the cognitive process of a human worker.

Goal Orientation: Organizational Objectives and Key Results​

Every effective employee works toward clear objectives:

  • Quarterly OKRs
  • Department KPIs
  • A project milestone

Similarly, every agent starts with a defined Goal. This goal acts as a "North Star," guiding decisions and prioritization.

Without a goal, both a human and an AI system drift into reactive task execution. With a goal, decisions become strategic.

Planning & Deconstruction: Project Planning​

When a manager assigns a complex objective, a strong employee doesn't try to complete it in one step. They:

  • Break the objective into subtasks
  • Identify dependencies
  • Sequence the work
  • Allocate resources

An AI agent does the same. It decomposes a high-level objective into smaller, manageable actions — essentially creating its own internal project plan.

This mirrors:

  • Work breakdown structures
  • Sprint planning
  • Implementation roadmaps

Dynamic Reasoning: Situational Judgment​

In the real world, plans rarely unfold perfectly.

A human worker constantly reassesses:

  • Is this approach working?
  • Has new information changed the situation?
  • Is there a better path?

AI agents do this through dynamic reasoning. As they execute their plan, they evaluate outcomes and adjust strategy based on new information.

This is similar to:

  • Risk management processes
  • Continuous improvement
  • Agile iteration

3. Interaction and Execution: The "Body" (How the Employee Gets Work Done)​

An employee can't just think — they must act within the organization's systems.

An AI agent also needs mechanisms to interact with its environment.

Tool Use: Enterprise Systems Access​

A human worker uses tools:

  • CRM systems
  • Finance dashboards
  • Email
  • Internal databases
  • APIs

They retrieve information and make updates.

An AI agent's "tool use" is the same concept. It calls APIs, queries databases, and updates external systems.

Without tool access, both a human and an agent are limited to theory. With tool access, they can create real operational impact.

The Feedback Loop: Performance Monitoring​

After taking action, a human employee checks the result:

  • Did the customer respond positively?
  • Did costs actually decrease?
  • Did the deployment succeed?

If something failed, they adjust.

This is the feedback loop — a core organizational concept reflected in:

  • PDCA (Plan-Do-Check-Act)
  • Retrospectives
  • Performance dashboards

An AI agent mirrors this behavior. After each action, it observes the result, updates its internal understanding, and decides the next step.

This is what allows adaptation instead of rigid execution.

4. Coordination and Governance: Organizational Structure​

As organizations scale, you don't rely on one employee to do everything. You introduce structure.

The same is true for agentic systems.

Orchestration & Multi-Agent Systems: Departments and Teams​

In a company:

  • A Research team gathers data
  • A Marketing team crafts messaging
  • A Finance team validates budgets
  • A Manager coordinates them

Each has a specialized role.

In a Multi-Agent System:

  • A "Research Agent" collects data
  • A "Writer Agent" synthesizes it
  • A "Manager Agent" coordinates workflow

Orchestration is equivalent to:

  • Project management
  • Workflow systems
  • Cross-functional coordination

It ensures the right work is done by the right "role" at the right time.

Agent Communication: Emails and Official Knowledge Bases​

One of the most essential components of a successful well working company is communication. Agents are also have the ability to communicate between each other and access external tools and resources.

A2A (Agent-to-Agent): Internal Emails, Slack, and Hand-offs​

In a company, employees communicate constantly:

  • A researcher sends findings to a strategist
  • A support rep escalates a case to engineering
  • A project manager assigns tasks to specialists

This is structured internal communication.

A2A communication enables the same behavior between agents.

Instead of working in isolation:

  • A "Research Agent" gathers data
  • It passes structured output to a "Writer Agent"
  • The Writer Agent refines it
  • A "Reviewer Agent" validates it

This is the digital equivalent of:

  • Email threads
  • Slack channels
  • Ticket hand-offs
  • Workflow transitions

The key idea is that communication is intentional and structured, not chaotic.

Just like in organizations:

  • Clear formatting reduces misunderstandings
  • Defined roles reduce duplication
  • Explicit hand-offs prevent dropped work

A2A allows agents to specialize and collaborate — just like departments inside a company.

MCP (Model Context Protocol): The Official Knowledge Base & Policy Handbook​

In any mature organization, there is an official source of truth:

  • A company wiki
  • SOP documentation
  • A policy handbook
  • A shared document repository

This ensures:

  • Everyone references the same data
  • Decisions are consistent
  • Processes are repeatable
  • Institutional knowledge is preserved

Model Context Protocol (MCP) serves a similar role for AI agents.

MCP standardizes how agents:

  • Access shared tools
  • Retrieve structured knowledge
  • Understand available capabilities
  • Connect to approved data sources

Instead of each agent improvising how to connect to systems, MCP acts like an official integration layer — a formalized knowledge and access protocol.

Think of it as:

  • The company's IT-approved software catalog
  • The internal wiki that defines procedures
  • The API contract that ensures everyone speaks the same language

It reduces fragmentation and ensures interoperability.

Human-in-the-Loop: Executive Approval​

Even senior employees escalate high-impact decisions:

  • Large financial commitments
  • Legal exposure
  • Strategic pivots

This is similar to Human-in-the-Loop (HITL) design.

In this model:

  • The agent handles execution and analysis
  • The human retains final decision authority for critical actions

This creates a hybrid workforce:

  • AI handles operational workload
  • Humans handle judgment, accountability, and strategy

Guardrails: Policies and Compliance​

Human employees operate within constraints:

  • Budget limits
  • Legal compliance
  • Security policies
  • Ethical standards

These are organizational guardrails.

AI agents also require explicit boundaries, such as:

  • "Do not exceed $50 in API usage."
  • "Do not access restricted systems."
  • "Do not expose sensitive data."

Guardrails are the system's version of governance policies.

5. Putting It All Together​

An AI agent is best understood not as "smarter automation," but as a digital employee embedded within your organizational structure.

It has:

  • A goal (like an OKR)
  • A reasoning process (like strategic thinking)
  • A plan (like a project roadmap)
  • Tools (like enterprise software access)
  • Feedback loops (like performance management)
  • Coordination layers (like management and departments)
  • Guardrails (like compliance policies)
  • Escalation paths (like executive approval)

The difference is not conceptual — it's architectural.

Agentic AI formalizes, in software, the same structures we already use to manage human work:

  • Goal setting
  • Planning
  • Execution
  • Monitoring
  • Governance

In that sense, deploying AI agents isn't inventing a new organizational paradigm.

It's extending the existing one — by adding digital workers that operate within the same principles that govern human teams.

Glossary​

Goal​

A desired outcome the agent is trying to achieve (e.g., "resolve a customer issue," "schedule a meeting," or "generate a report"). Goals guide how the agent plans and prioritizes actions.

Actions​

The operations an agent can perform in its environment, such as sending messages, calling APIs, updating records, generating content, or triggering workflows.

Tools​

A capability or external system an AI agent can use to take action or retrieve information, such as APIs, databases, business applications, calculators, or workflows. Tools allow agents to go beyond conversation and perform real tasks.

Reasoning​

The agent's ability to analyze information, make decisions, and choose actions. Reasoning may involve logic, rules, probabilistic thinking, or large language models (LLMs).

Planning​

The process of deciding a sequence of steps needed to achieve a goal. A plan may change dynamically as new information becomes available.

Feedback Loop​

A mechanism where an agent evaluates the outcome of its actions and adjusts future behavior accordingly. Feedback loops help agents improve performance over time.

Tool Use​

The ability of an AI agent to call external tools or systems, such as databases, calculators, APIs, or enterprise software, to complete tasks beyond text generation.

Orchestration​

The coordination of multiple agents, tools, or workflows to accomplish a larger task. Orchestration ensures the right components act in the right order.

Multi-Agent System​

A setup where multiple AI agents work together, each with specialized roles, to solve complex problems more efficiently than a single agent.

Human-in-the-Loop​

A design approach where humans review, guide, or approve an agent's actions, especially for sensitive or high-impact decisions.

Guardrails​

Rules, constraints, or policies that limit what an AI agent is allowed to do. Guardrails help ensure safety, compliance, and predictable behavior.



Agentic Workflows


Contributors​

Robledo, Francisco
Robledo, Francisco