Agent Breakthrough: A Complete Guide to Core Development Workflow

Contents

• Agent Overview
• Core Evolution and Key Modules of Agents
• Building an Agent Based on a Large Model (Developer’s Perspective)
• Evaluating an Agent

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Introduction: From Data to Decision‑Making

When data can think and code can make autonomous decisions, we shift from ordinary workflows to Agentic AI. But how does a fully autonomous data analyst come into being?

After two years of iteration and deep research — refining architecture design, verifying in practice — we have distilled the core blueprint for Agent development.

This guide walks you through the Agent’s “mind” and “body”: planning, memory, tool orchestration, context engineering — showing how to gradually construct an entity that truly thinks and acts.

Whether you are curious, learning, or experimenting as a peer, you’ll find architectural insights and actionable practices rooted in real-world projects.

> 📝 Every section is distilled from actual project experience — autonomy in AI is no longer a distant ideal.

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A Fully Autonomous Agentic AI Data Analyst — Possible?

Yes.

I have worked on AI Agent product development for more than two years — from the OlaChat web assistant, to a coding copilot, to a data analysis Agent called Dola.

Dola (by Tencent PCG Big Data Platform) is a new‑generation, Agent‑powered data analysis assistant. You provide a data table — Dola acts as your dedicated AI analyst.

What Dola Can Do:

• Autonomous complex analysis: anomaly attribution, persona comparison, stock/fund back‑testing, housing price prediction.
• SQL automation: write SQL from scratch, fix errors, execute queries.
• Python processing & visualization.
• Generate complete reports — no coding needed; you interact in natural language.

This means Dola can autonomously take on real analyst and operations tasks.

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01 — Agent Overview

1.1 What is an Agent?

An Agent — an intelligent entity capable of:

• Perceiving its environment (e.g., multimodal input like vision or voice).
• Autonomous decision‑making (via deep learning / reinforcement learning).
• Executing tasks (via APIs, tools, or physical devices).
• Continuous learning and evolution (adaptation over time).

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1.2 Basic Framework

Early AI Agent architecture looked like this:

Over time, these evolved into modern Agentic AI designs — adding:

• Memory modules
• Advanced context management
• Autonomous tool use

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💡 Tip for Teams Building Production Agents

If your Agent outputs need to reach audiences quickly across platforms, consider open‑source solutions like AiToEarn官网:

An AI content monetization platform with:

• AI content generation tools
• Cross‑platform publishing
• Analytics
• For publishing directly to Douyin, Kwai, WeChat, Bilibili, Rednote, Facebook, Instagram, YouTube, X (Twitter).

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Core Components of an AI Agent

AI Agent = Brain (LLM) + Memory + Tool Use + Planning

Large model development is shifting focus from content intelligence to behavior intelligence, aiming for AGI capabilities:

• Dialogue
• Reasoning
• Autonomous scheduling
• Innovation
• Organization

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1.3 Agent Classification

Types:

• Reflection Mode — Self‑refine after executing tasks.
• Tool Use — Invoke external tools/APIs.
• Planning Mode — Organize tasks upfront.
• Multi‑Agent Collaboration — Agents cooperate to achieve goals.

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1.4 Agent Development Stages

Multi‑agent collaboration progress:

• Early Exploration (Early–Mid 2023) — Basic exchange and simple parallelism.
• Framework Maturity (Mid 2023–Mid 2024) — e.g., Microsoft AutoGen’s conversable agents.
• Application Deepening (Mid 2024–2025) — Domain‑specific agents, complex workflows.

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Main Frameworks

• AutoGen — highly customizable dialogue‑based agents.
• LangGraph — precise process control.
• Crew AI — quick team collaboration systems.

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💡 Note on Framework Choice

LangChain and similar can be code‑heavy; self‑built frameworks may offer more autonomy.

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02 — Evolution & Core Modules

Early Agents resembled workflows — pre‑defined steps executed in sequence.

Workflows are rigid; Agents explore solutions autonomously.

Key Modules:

• Planning
• Memory
• Tool scheduling (Function calls / MCP)
• Execution

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Planning Module

Purpose:

• Task decomposition — break into subtasks (e.g., CoT reasoning).
• Reflection and refinement — learn from past steps (e.g., ReAct loop: Think → Act → Observe → Answer).

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Memory System

Why:

• Large models forget due to limited context windows.
• Layers:
• Short‑term — current session.
• Mid‑term — topic summaries.
• Long‑term — user profiles, accumulated knowledge (via vector DB + RAG).

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Tool / Function Scheduling (Function Call)

Enables:

• Real‑time, up‑to‑date info retrieval.
• Specialized actions through APIs/software.

Risks:

• API failure.
• Privacy breaches.
• Multistep failures.

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MCP Protocol

Standardized tool/resource integration.

Host ↔ MCP Client ↔ MCP Server — manage Resources, Tools, Prompts.

Advantages:

• Rapid tool onboarding.
• Ecosystem compatibility.

Limitations:

• Log tracing difficulty.
• Possible performance bottlenecks.
• Output format inconsistency.

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03 — Building an Agent on a Large Model

Effectiveness depends on:

• Framework design — single vs multi‑Agent based on scenario.
• Context engineering — beyond prompt writing, includes memory and multi‑turn dialogue management.

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Context Engineering Principles

• Optimize KV‑cache hit rate.
• Use dynamic, state‑aware prompts.
• Extend context using file systems.
• Maintain focus via goal restatement.
• Preserve errors for learning.
• Avoid few‑shot overfitting.
• Ensure terminology consistency.
• Dynamically adjust prompts.
• Define tool capability boundaries.
• Control feedback granularity.
• Balance long‑term memory with short‑term context.
• Set human intervention thresholds.
• Use multi‑Agent competitive validation.

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Memory System Implementation

Layer data: recent actions, thematic summaries, verified long‑term facts.

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Function Call Flow

• User request → App system.
• Prompt composition → LLM.
• Tool decision → Approval → Execution.
• Result integration → Natural language reply.

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MCP Flow

Similar, but tools/resources are managed via MCP server—eliminates need for manual API integration.

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Multi‑Agent Applications

Enhance problem‑solving via context division and collaboration protocols.

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04 — Evaluating Agents

Benchmarks:

• AgentBench
• InfoQuest
• MINT
• ToolBench
• GTA
• ToolDial
• AgentBoard
• WorkBench
• DataSciBench

Evaluation remains complex—especially in multimodal, multi‑turn scenarios.

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References

See original list — includes research papers, surveys, frameworks (LangChain, AutoGen, CrewAI, MemoryOS).

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Team & Community

Qiyu Team — 360 Group

Largest frontend team, offering career paths and training for:

• Engineers
• Lecturers
• Translators
• Leaders

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💡 If interested in frontend + AI, scan QR code to join the discussion group.

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Final Note

For deploying Agents with cross‑platform content output, AiToEarn offers:

• AI generation tools
• Simultaneous publishing (Douyin, Kwai, WeChat, Bilibili, Rednote, Instagram, YouTube…)
• Analytics & model ranking (AI模型排名)

It connects cutting‑edge Agent capabilities with real business value.

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Would you like me to create a flowchart cheat‑sheet summarizing the Planning → Memory → Tool Use → Execution pipeline for quick reference?