Python phone AWS AgentCore age

Summary
Job Title:
Python phone AWS AgentCore agent

Job Type: Fixed-Price

Project Overview
I am looking for an experienced AWS and Python developer to build a scalable Voice AI Agent architecture using AWS Bedrock AgentCore. This is fundamentally an educational project: your goal is to build a fully working example system, document it exhaustively, and teach me how to reproduce, modify, and deploy it completely independently from scratch.
Develop Real Estate agent (sell homes).
You may use existing open-source GitHub projects as a foundation, provided the final deliverable meets all my requirements and is fully documented.

System Architecture & Core Examples
We need a one Canadian phone voice agent system acting as customer service

The Use-Case Examples:
Think of these as sales agents for stores or services , where store has many unique products, for example :

Or Case 1: Food Catering

Or case 2: Cleaning Services

Or case 3: Refrigerator Repair
Or case 4 : Real Estate agent (sell homes)

Expected Interaction Flow:

At the beginning of the call, the agent asks the user what they want to talk about (essentially asking which file to add to system prompt ).

Example A (Case 3): A user calls for Store 3 and says, "I have Refrigerator abc123." The agent must dynamically retrieve the specific .txt skill file for "Refrigerator abc123" and use this skill for the remainder of the session.

Example B (Case 2): A user calls for Store 2 and says, "I need to clean a sofa." The agent chooses the specific .txt skill file on how to clean a sofa and uses it for the session.
Example C (Case 3) : user wants to talk about specific home for sale . Find skills file with specific address what user tells you .

Each .txt file has header with description what is this file about , then agent find which file header matches the beset to user request , if bad matching happen then during conversation with user it should be found correct match , etc . Usual skills router implementation

Technical Features & Requirements
Voice Integration: Phone call voice chat using a Canadian phone number (+1). One phone number serves exactly one agent/store.

Strict Data Isolation: agent has its own dedicated group of .txt knowledge files.

Session Memory: The agent must retain context during the call (e.g., if the caller says "My name is Peter," the agent calls him Peter for the whole session). There must be no memory between different sessions.

Logging: Detailed logs must be generated and saved for every individual call/session.

Technical Stack
Language: Python

AI Framework: AWS Bedrock AgentCore

Telephony: low cost AWS services compatible with AgentCore (e.g., Amazon Connect, Chime SDK, or Twilio)

Infrastructure: AWS

Strict Development Constraints
Your Own Environment: You must develop, record, and test this entirely in your own authorized AWS account. I will not provide my AWS account or LLM credentials (openai or gemini ).

Zero Dependencies: My later deployment must not depend on your accounts, resources, or API keys.
Use openai or gemini for LLM (I am open for suggestions for other good LLM providers )
Deliverables (Required for Payment)
This fixed-price project covers the application, deployment, security, tests, source code, reporting, manuals, videos, and a handover session.

1. Editable Word (.docx) Manual:

Must start from a totally clean environment.

Every actionable setup/configuration step needs a real, readable screenshot.

Numbered instructions with copyable commands/settings.

Explain the purpose of the step, the expected result, and how to verify it.

Clearly distinguish between Windows and Server commands and explain any placeholders (e.g., [YOUR_BUCKET_NAME]).

Must cover: Prerequisites, code structure, AgentCore setup, memory/RAG routing, telephony settings, database/logs, AWS IAM/security, tests, monitoring, recovery, updates, cost breakdown, and complete environment cleanup.

Include exact software/library versions and architecture details. Do not include real secrets/passwords in the doc. Include a section on common errors and fixes.

2. Narrated MP4 Videos:

Follow the exact section/step numbers of the .docx manual.

Include a timestamp index.

Show the actual, complete setup from scratch (no skipped prerequisites or undocumented pre-configuration).

Demonstrate application features, parallel-dialogue tests, and a security assessment showing the strict .txt file isolation.

Note: Videos, screenshots, and code must match perfectly.

3. Handover & Teaching:

A session (or clear video guidance) teaching me how to modify both the Python application codebase and the Python test suite.

Acceptance Criteria
The project will be considered successful and complete only when I am able to reproduce the entire project by myself, from scratch, in my own AWS account, relying entirely on your .docx manual and videos without needing to ask you for missing steps.

Very good natural voices