I had the chance to take part in the 5-Day AI Agents Intensive last week, which greatly increased my comprehension of how contemporary AI agents are created, assessed, and implemented at scale. Even though I had previously worked with agents, this intensive provided me with fresh approaches, more understandable frameworks, and useful tactics that have completely changed the way I approach developing AI agents.
The combination of codelabs, whitepapers, instructor-led livestreams, and practical exercises offered a structured learning experience that felt thorough and instantly applicable over the course of the five days. More importantly, the program emphasized practical development workflows using Gemini models and the Agent Development Kit (ADK)โallowing me to not only learn concepts, but also build real working prototypes.
What I Learned Across the 5 Days
๐ฆ Day 1 โ Introduction to Agents
The program started by reframing what AI agents truly are: systems capable of autonomous decision-making, tool use, and goal-driven execution. This session clarified many foundational concepts and introduced a modular way of thinking about agent capabilities.
๐ฉ Day 2 โ Agent Tools & MCP Interoperability
This day opened my eyes to how powerful AI agents become when connected to real-world tools. Learning about MCP (Model Context Protocol) and how to integrate external tools into an agentโs workflow gave me a new method for designing highly extensible agents.
๐จ Day 3 โ Context Engineering (Sessions & Memory)
One of the most transformative topics. Understanding how to structure sessions, working memory, and long-term memory helped me rethink how an agent maintains continuity, learns from past interactions, and adapts in real time.
๐ง Day 4 โ Agent Quality & Evaluation
I learned new frameworks for measuring agent qualityโusing structured evaluation metrics, scenario-based assessments, and iterative refinement. This changed the way I validate agent reliability and safety.
๐ฅ Day 5 โ From Prototype to Production
The week concluded with deploying agents using Vertex AI Agent Engine, focusing on scalability, monitoring, and production readiness. This provided the missing link between experimentation and real-world deployment.
Key Takeaways That Changed My Workflow
- A practical method for building functional single-agent and multi-agent workflows
- Deep understanding of tool integration using MCP
- Designing agents with session-based context, short-term memory, and long-term memory
- Evaluating and improving agent performance using structured metrics and test scenarios
- Deploying AI agents into production using Vertex AI Agent Engine
A New Approach to Agent Development
Beyond the technical lessons, the 5-Day Intensive gave me a renewed perspective on how AI agents should be architected. I now approach agent design with:
- clearer role definitions,
- stronger emphasis on context awareness,
- systematic evaluation workflows, and
- a more production-oriented mindset.
Future AI-driven solutions, such as automation systems, intelligent workflows, and multi-agent architectures, will be directly impacted by these new techniques and mental models.
๐ฎ COMPLAINT AGENT โ CITIZEN COMPLAINT AUTOMATION SYSTEM
๐ PROJECT DESCRIPTION
This project contains the core logic for the Complaint Agent (complaint_agent), a system designed to streamline and automate citizen complaint handling. The agent is built using the Google Agent Development Kit (ADK) and focuses on immediate classification, routing, and monitoring of public feedback to ensure swift governmental response.
๐ USE CASE: CITIZEN COMPLAINT AND REPORTING AGENT
Description: Automatically receives and classifies citizen reports/complaints by urgency, routes them to the correct governmental department, and monitors follow-up status until resolution.
Benefit: Ensures rapid response and accountability in handling public issues.
โ PROBLEM STATEMENT
Manually managing citizen reports is inherently inefficient and prone to error. Public complaints arrive through diverse channels (social media, calls, web forms) and must be manually reviewed, categorized, prioritized (urgency), and forwarded to the correct departmental unit (e.g., Public Works vs. Environment).
This manual triage process leads to:
Response Delays: Slow classification means critical issues (like infrastructure failure) face significant latency.
Misrouting: Mistakes in identifying the responsible agency lead to circular referrals and citizen frustration.
Lack of Accountability: Without automated tracking, monitoring the status and completion of a follow-up action is difficult, reducing public trust.
๐ก SOLUTION STATEMENT
AI Agents can automatically process incoming reports, overcoming the limitations of manual triage. The Complaint Agent (complaint_agent) uses the Gemini model's reasoning capabilities combined with specialized tools to:
Analyze and Extract: Instantly read the complaint text (from any input format) and extract key entities, location, severity, and implied category (e.g., Infrastructure, Health, Administration).
Automate Triage: Use predefined tools/rules to match the extracted category against the governmental organizational structure, determining the correct responsible Department (Agency) and prioritizing the response queue (High / Medium / Low urgency).
Transparent Monitoring: Provide an automated interface for citizens to check the real-time status of their reports using a unique Report ID, and enable authorized updates to the report lifecycle, ensuring full transparency from submission to resolution.
System Integration: Automatically log the complaint into the central database and trigger notifications to the responsible unit, effectively turning the manual administrative chore into a streamlined, data-driven workflow.
๐๏ธ ARCHITECTURE OVERVIEW
The core of this system is the Complaint Agent (complaint_agent), which functions as the primary orchestrator. Its definition includes:
model: Utilizing a fast, reliable model like gemini-2.5-flash for rapid classification and reasoning.
system_instruction: A strict set of instructions guiding the agent to prioritize information extraction and compulsory tool use.
tools: Essential utilities defined to interact with external government systems and knowledge bases.
๐ Workflow โ Chain of Responsibility:
The Agent analyzes the report, determines the category and urgency, and uses its specialized tools to validate the routing path and record the data.
๐งฐ ESSENTIAL TOOLS AND UTILITIES (5 Tools)
The complaint_agent is equipped with specific, reliable tools to interact with backend government systems.
Routing Logic (
tentukan_unit_penanggung_jawab)
๐งญ Function: Receives the raw category and returns the official name of the responsible Department (e.g., "Public Works Department (PWD)").Complaint Logging (
catat_laporan)
๐๏ธ Function: Simulates logging the data into the central complaint database and generates a unique Report ID (e.g., LAP-4521).Urgency Checker (
check_urgency_level)
โฑ๏ธ Function: Analyzes the description of a report to classify its urgency (High, Medium, or Low) based on predefined keywords.Knowledge Base Search (
search_knowledge_base)
๐ Function: Allows the Agent to search for answers, FAQs, or procedural guidance for general inquiries.Status Monitor & Updater (
monitor_and_update_status)
๐ Function: Enables the Agent to both retrieve the current status of an existing report (using its unique Report ID) and update that status upon authorization.Location Verifier (
verify_incident_location)
๐ Function: Verifies and determines the precise geographical coordinates of the reported incident to provide accurate location data to the response team.
๐งพ CONCLUSION
The Citizen Complaint and Reporting Agent demonstrates the immediate value of AI Agents in enhancing public service delivery. The system transforms the time-consuming and error-prone process of manual report triage into a fast, transparent, and accountable workflow.
๐ ๐ณ๐๐๐ ๐ท๐๐๐๐๐๐: Github
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