Autonomous Career Agent (Agentic AI Application)

Project Overview

Autonomous Career Agent (ACA) is the capstone project for the 5-Day AI Agents Intensive with Google (Nov 2025). It embodies a multi-agent architecture that automates key elements of job hunting — from discovering vacancies to analyzing fit and generating tailored application documents — while illustrating the practical engineering decisions involved in building reliable agentic workflows.

NoteRole

AI Systems Developer

NoteTools

Python, Google Agents Development Kit (ADK), Vertex AI, Google Cloud Storage

NoteDomain

AI agent design, automation, applied LLM pipelines

Key Features & Components

Multi-agent orchestration architecture

Implements an orchestrator coordinating specialized agents for search, assessment, and document creation, separating high-level reasoning from tool execution

Automated job discovery

Uses external tools and APIs to fetch relevant vacancies based on user criteria, bridging structured systems and unstructured real-world search logic

Candidate–job fit analysis

Evaluates a user’s experience against job postings to score compatibility and highlight relevant skills, enabling data-informed application decisions

AI-generated application documents

Produces context-aware CVs and cover letters using structured LLM prompting, tailored per vacancy

Secure artifact delivery

Uploads outputs to Google Cloud Storage and presents time-limited download links, demonstrating cloud-integrated workflows and secure access patterns

Agentic vs deterministic workflow

The architecture serves as a concrete case study comparing the flexibility and reasoning capabilities of agentic systems against traditional step-by-step deterministic pipelines for real-world tasks

Read more about the workflow comparison in my blog post.

Implementation

Orchestrator pattern

Central coordinator that maintains workflow state and delegates tasks to specialist agents, reducing token cost and hallucination risk by keeping context structured

Modular agent design

Stands up search, assessment, and generation agents with clear contracts and testable components, aligning with robust software engineering practices

Hybrid cloud integration

Bridges agent outputs with real-world delivery pipelines using Google Cloud Storage for secure and scalable artifact access

Observability & testing

Demonstrates observability layers and evaluation pipelines, which are critical in agentic systems where traditional deterministic testing is insufficient

Outcomes & Impact

Shows the value and cost of agentic architecture compared to deterministic workflows, helping clarify when autonomous reasoning adds real advantage.

Practical automation of job search workflows far beyond simple rule-based scripting, enabling nuanced interpretation of unstructured text.

Demonstrates engineering maturity in building modular AI systems, integrating LLM reasoning with external tooling and cloud services.

Skills Demonstrated

LLM orchestration • Multi-agent architecture • AI workflows vs deterministic pipelines • Python systems engineering • Google ADK • Vertex AI • Cloud-integrated artifact delivery • Observability & evaluation of agentic systems

Apply This to Your Business

If your business needs help designing or building AI agent systems to automate complex workflows, let’s talk.

See Also