Abhi

Posted on Nov 24

Agents that ship: Breakdown of the 3-part architecture that survived real-world chaos

#webdev #ai #programming #tutorial

1. The Big Picture: From Passive AI to Autonomous Agents

Historical Context

Traditional AI was passive — it responded to prompts, answered questions, or translated text.
The new wave is about autonomous, goal-oriented AI agents — systems that plan, act, and solve complex problems over multiple steps without constant supervision.

The Core Idea

Agents don’t just talk — they act.
They execute actions in the real (or digital) world to achieve defined goals.

(Agentic AI problem-solving process from Whitepaper - Introduction to Agents and Agent architectures)

2. The Agent Anatomy: Three Core Parts

The white paper breaks down an agent into three key components:

The Model (Brain) – The reasoning and decision-making core.
The Tools (Hands) – The interfaces to act on the world.
The Orchestration Layer (Conductor) – The system that coordinates everything.

A. The Model – “The Brain”

The LLM (Language Model) serves as the reasoning engine.
Its main function: managing the context window — constantly deciding what’s important right now from:
- The mission goal
- Memory
- Tool outputs

It determines what matters for the next reasoning step.

B. The Tools – “The Hands”

Tools are how agents interact with the outside world — APIs, functions, databases, vector stores, etc.
Examples:
- Look up customer data
- Check inventory
- Query a vector database

The model decides which tool to use, while the orchestration layer executes the call and feeds results back into the model.

C. The Orchestration Layer – “The Conductor”

Governs the entire reasoning loop:
- Planning
- Memory/state management
- Reasoning strategy (e.g., Chain-of-Thought, ReAct)

The ReAct Loop

Think: Based on the goal, decide next step.
Act: Use a tool.
Observe: Take in the result.
Think again: Iterate.

This think–act–observe loop is what transforms an LLM into a true agent capable of executing complex, adaptive workflows.

3. Example: The Agentic Loop in Action

Scenario: Organizing a Team’s Travel

Mission: “Organize my team’s travel.”
Scan the Scene: Identify tools — calendar, booking APIs, etc.
Plan: “First, get the team roster.”
Act: Call getTeamRoster() tool.
Observe & Iterate:

Receive team list → update context.
Next step: check availability, then book travel.

This cycle continues until the mission is completed.

4. Levels of Agent Capability (Taxonomy)

(Agent Taxonomy Levels from Whitepaper - Introduction to Agents and Agent architectures)

Designing an agent requires defining its capability level:

Level 0: Basic LLM

Just the model.
No tools or external access.
Can explain concepts but cannot access real-time data.

Level 1: Connected Problem Solver

Model + Tools.
Gains real-world awareness.
Example: Looks up current sports scores via a search API.

Level 2: Strategic Problem Solver

Handles multi-step tasks using context engineering.
Example: “Find a coffee shop halfway between two addresses.”
- Uses a map tool → gets midpoint coordinates.
- Then queries coffee shops near that point with ratings >4.0.

Level 3: Collaborative Multi-Agent System

A team of agents working together.
Example:
- Project Manager Agent → delegates to
- Market Research Agent
- Data Analysis Agent
Enables goal delegation and independent sub-agent reasoning.

Level 4: Self-Evolving System

Agents that identify and fill their own capability gaps.
Example:
- Realizes it needs social media sentiment analysis.
- Creates a new agent to perform that task.
- Configures access and integrates it automatically.

5. Building Reliable Production-Grade Agents

Model Selection

Don’t just chase benchmarks.
Choose models that are:
- Strong in reasoning.
- Reliable with tool usage.
Use Model Routing:
- Heavy reasoning → Gemini 1.5 Pro.
- Simple tasks → Gemini 1.5 Flash.
- Balances cost and performance.

Tool Design

Two main categories:

Retrieval Tools (RAG, Vector DBs) – Ground the agent in factual data.
Action Tools (APIs, Scripts) – Allow real-world execution.

Function Calling

Tools must have clear specifications (e.g., OpenAPI format).
The model must know:
- What the tool does.
- What parameters it requires.
- What output to expect.

This ensures the loop stays stable and accurate.

Memory Management

Short-Term Memory: Current context and reasoning trace for the task.
Long-Term Memory: Persistent storage — preferences, user history, learned data. Often implemented via vector databases as RAG tools.

6. Testing and Debugging (AgentOps)

Evaluation

Traditional testing doesn’t work — outputs vary.
Use AI-as-a-judge:
- Another model grades outputs against a rubric.
- Checks factual grounding and adherence to constraints.

Observability

OpenTelemetry Traces track every step:
- Prompts, reasoning, tools used, parameters, outputs.
Acts as a flight recorder for debugging.

User Feedback

Every failure → a new test case.
Builds a “golden dataset” that prevents recurring issues.

7. Security and Governance

The Trust Trade-Off

More capabilities = more risk.
Requires Defense-in-Depth:
- Hard-coded guardrails (policy engines).
- AI-based guard models to detect risky behavior pre-execution.

Agent Identity

Each agent needs a secure digital identity (e.g., SXBF standard).
Enables least-privilege access control — limit what each agent can do.

Agent Governance

Prevent agent sprawl with a central control plane:
- Routes all traffic (user ↔ agent, agent ↔ tool).
- Enforces policies and authentication.
- Monitors logs and performance metrics.

8. Continuous Learning and Adaptation

Agents evolve through:

Runtime logs and traces
User feedback
Policy and data updates

Simulation Environments (“Agent Gym”)

Safe sandbox for testing complex multi-agent behaviors.
Enables experimentation with synthetic data before deployment.

9. Real-World Examples

Google Co-Scientist

A Level 3–4 system for scientific research.
Acts as a virtual collaborator:
- Formulates hypotheses.
- Designs experiments.
- Analyzes data.
Uses multiple agents under a supervisor agent.

(The AI co-scientist design system from Whitepaper - Introduction to Agents and Agent architectures)

AlphaVolve

A Level 4 AI system focused on algorithm discovery.
Generates, tests, and evolves algorithms autonomously.
Has achieved improvements in:
- Data center efficiency.
- Matrix multiplication algorithms.
Humans guide the process by defining evaluation metrics.

(Alpha Evolve design system )