🤖 Agentic AI

What Is Agentic AI?

An Agentic AI system decomposes a complex task into smaller executable steps and coordinates them through an orchestration workflow.

Instead of:

graph TD
    A[User Goal] --> B[LLM]
    B --> C[Final Output]

we now have:

graph TD
    A[User Goal] --> B[🗓️ Planner]
    B --> C[🧰 Tool Selection]
    C --> D[📝 Information Retrieval]
    D --> E[🤔 Reasoning]
    E --> F[💡 Self-Evaluation]
    F --> G[📋 Revision]
    G --> H[Final Output]

From Prompting to Cognitive Workflows

Agentic AI workflows break complex tasks into smaller steps that are executed iteratively

• Similar to how humans approach complex work with thinking, research, and revision.

Traditional LLM applications force the model into a highly constrained execution pattern

single-shot text generation

• no intermediate planning
• no reflection
• no retrieval refinement
• no verification
• no iterative correction

It is equivalent to asking a human to write an entire technical report in one pass without:

• outlining
• researching
• revising
• fact-checking
• editing

All in one pass:

$$P(Task)$$

Humans do not work that way.

High-performing AI systems increasingly do not either.

Agentic AI systems instead use:

iterative reasoning workflows

Complex reasoning tasks become easier when broken into smaller steps.

This significantly increases reliability.

$$P(Task) = \prod_{i=1}^{n} P(Subtask_i)$$

The key architectural shift is this:

Intelligence increasingly emerges from workflow structure rather than model size alone.

Traditional LLM vs Agentic systems

💬 Traditional LLM Apps	🤖 Agentic AI Systems
Single inference	Multi-step execution
Stateless	Stateful workflows
Minimal reasoning depth	Iterative reasoning
No reflection	Self-critique loops
Limited tool use	Extensive tool orchestration
Prompt-centric	Workflow-centric

---

Agentic AI Components

1. 🗓️ Planner

Planning allows agents to dynamically determine the sequence of actions needed to complete a task.

• The planner decomposes a high-level objective into executable subtasks.
• Developers provide tools; the agent determines how to use them.
• The approach enables greater autonomy and flexibility than fixed workflows. Without planning:

Developer decides workflow

With planning:

Agent decides workflow

This significantly increases flexibility.

Planing in Highly Autonomus Agent

1. Traditional Planing

Uses JSON & Tools to create plan and execute sequentially.

graph TD 
    A[User Question] --> B[Planner] --> C[Tool 1] --> D[Tool 2] --> E[Tool 3] --> F[Answer]

Example:

{
  "plan": [
    {"step": "Search Web", "tool": "WebSearchAPI", "input": "What is the capital of France?"},
    {"step": "Extract Info", "tool": "TextExtractor", "input": "Web Search Results"},
    {"step": "Generate Answer", "tool": "LLM", "input": "Extracted Information"}
  ]
}

2. Planning with Code

Uses Code Execution as the primary planning mechanism.

The planner effectively becomes a meta-agent that writes the agent's own code on demand.

• Each line of code represents a planning step.
• This allows for more complex logic, loops, and conditionals than static JSON plans.

graph TD

    A[User Question]

A --> B[LLM]

B --> C[Generate Python]

C --> D[Execute Code]

D --> E[Result]

E --> F[Answer]

Example:

def agent_plan(question):
    if "capital" in question:
        return "import requests\nresponse = requests.get('https://api.example.com/search?q=' + question)\nprint(response.json())"
    else:
        return "print('I don't know how to answer that yet.)"

Code Outperform JSON Plans

Code acts as both the plan and the implementation.

$$Code > JSON > Plain\ Text$$

Python already provides thousands of functions contain enormous amounts of functionality.

• Pandas
• NumPy
• Scikit-Learn
• Matplotlib
• Requests

This approach reduces the need for large collections of custom tools.

Rather than inventing custom tools we can leverage existing libraries as tools.

2. 🧰 Tool Invocation Layer

Tools are external systems that provide capabilities beyond the LLM's internal knowledge and reasoning.

LLMs alone are limited by:

• static training data
• context window constraints
• hallucination risk

Tool usage extends capabilities dynamically.

Agents become significantly more powerful once they can interact with external systems.

Typical tools include:

• Web search
• Databases
• Vector stores
• APIs
• Code interpreters
• Browsers
• Internal enterprise systems

The workflow evolves into:

graph TD
    A[User Goal] --> B[Planner]
    B --> C[Search Tool]
    B --> D[Database]
    B --> E[Code Executor]
    C --> F[LLM Reasoning]
    D --> F
    E --> F
    F --> G[Evaluator]
    G --> H[Final Output]

Tools are just code that the LLM can request to be executed

3. 💡 Iterative Reasoning Loops

Instead of generating a final answer immediately, the system continuously improves intermediate outputs.

Mathematically, we can think of the workflow as an optimization process:

$$x_{t+1} = f(x_t, r_t, e_t)$$

Where:

• $x_t$ = current state/output
• $r_t$ = retrieved context
• $e_t$ = evaluation feedback
• $f$ = reasoning transformation

This resembles gradient-style iterative optimization, except the optimization occurs over reasoning trajectories rather than numerical parameter space.

Feedback Loops Improve Quality

Iterative refinement approximates deliberate reasoning.

This creates better outputs than single-pass generation.

The system becomes less like a chatbot and more like a distributed cognitive pipeline.

graph TD
    A[Build Workflow] --> B[Run Agent]
    B --> C[Collect Outputs]
    C --> D[Error Analysis]
    D --> E[Design Evals]
    E --> F[Improve System]
    F --> B

Evaluation discipline

Evaluation discipline is the biggest differences between mediocre AI systems and production-grade agentic systems not the model.

In practice, the ability to systematically evaluate, debug, and improve an agentic workflow is often the strongest predictor of whether a team can build reliable AI systems at scale.

Its difficult because you rarely know ahead of time what will fail.

4. 📋 Reflection and Self-Critique

The system evaluates its own outputs and identifies weaknesses.

Modern agents often use reflection loops:

graph TD
    A[Draft Output] --> B[Critique]
    B --> C[Identify Weaknesses]
    C --> D[Revise]
    D --> A

This dramatically improves:

• factual consistency
• coherence
• reasoning depth
• code quality
• planning accuracy

The important insight:

The evaluator is often as important as the generator.

---

The Real Engineering Challenge

Most people think Agentic AI is primarily about prompting.

In practice, the hardest problems are:

• orchestration
• state management
• memory handling
• tool reliability
• retry logic
• evaluation
• cost optimization
• latency control
• permission boundaries

Prompt engineering becomes only one layer of a much larger system.

Emerging Architectural Patterns

1. Planner-Executor Pattern

The planner decomposes tasks, while the executor handles execution and tool calls.

graph TD
    A[Goal] --> B[Planner]
    B --> C[Task Queue]
    C --> D[Executor]
    D --> E[Tool Calls]
    E --> F[Results]
    

2. Multi-Agent Collaboration

Different agents specialize in different domains.

Example:

• Research agent
• Coding agent
• Reviewer agent
• Compliance agent

graph LR
    A[Coordinator] --> B[Research Agent]
    A --> C[Coding Agent]
    A --> D[Reviewer Agent]
    B --> E[Shared Memory]
    C --> E
    D --> E
    

3. Human-in-the-Loop Systems

Manual oversight remains crucial for high-stakes applications.

Many production systems insert approval checkpoints for:

• compliance
• legal review
• financial decisions
• healthcare workflows

This hybrid architecture is usually more practical and safer.

Agentic AI Is a Systems Engineering Problem

The future of AI engineering is increasingly shifting from:

"How do I write a better prompt?"

to:

"How do I design a better reasoning workflow?"

That is a profound transition.

The competitive advantage is no longer just:

• larger models
• larger context windows
• better prompting

It increasingly comes from:

• orchestration quality
• retrieval strategy
• evaluation loops
• workflow architecture
• system reliability

The most powerful AI applications over the next few years will likely not be single models.

They will be coordinated cognitive systems.

And that changes the role of software engineering entirely.

---

RAG + Agents

RAG systems provide external memory.

Agentic systems add:

• planning
• reasoning
• dynamic retrieval
• adaptive execution

A modern research agent may:

1. Generate search queries
2. Retrieve documents
3. Rank relevance
4. Summarize findings
5. Detect knowledge gaps
6. Retrieve additional context
7. Revise conclusions

This creates recursive information acquisition loops.

Final Thought

We are moving from:

$$LLM \approx Text Generator$$

toward:

$$AI\ System \approx Cognitive Operating System$$

That distinction may define the next generation of software architecture.