Build MVP AI Agent Backend with API, Logging, Retries, Docker, and PostgreSQL
Budget: $25 – $50 USD
We need an experienced AI agent engineer to build a focused MVP foundation for a production-oriented AI agent.
The goal is not to build a full enterprise agent platform, but to create a clean working structure that can be extended safely in later phases.
Exact Deliverables
1. Working AI agent API endpoint
o Python backend, preferably FastAPI
o API-first structure
o Accepts user input and returns an LLM-generated response
o Clear request/response format
2. Basic LLM orchestration
o OpenAI, Anthropic, or similar LLM API integration
o Configurable system prompt / instruction layer
o Simple agent workflow logic
o Basic tool/function structure if required
3. PostgreSQL logging
o Store request, response, timestamp, status, and errors
o Simple database schema
o Setup or migration script included
4. Retry and error handling
o Basic retry logic for failed LLM/API calls
o Timeout handling
o Clear API error responses
5. Dockerized setup
o Dockerfile
o docker-compose setup for backend + PostgreSQL
o Environment variable configuration
o Local run instructions
6. Simple admin/status view or admin API
o Basic endpoint or minimal page to review recent agent runs
o Shows logs, statuses, and errors
o No complex dashboard required
7. Handoff documentation
o Local setup instructions
o API usage example
o Database setup steps
o Notes on next-phase improvements
Application Requirement
Please do not apply with only a written explanation.
When applying, include at least one of the following:
• Live demo URL of a previous AI agent, LLM backend, RAG, or automation system
• Short Loom/video walkthrough of a relevant project
• GitHub repo or code sample showing agent/API/backend structure
• System architecture diagram from a similar project
• Screenshots of a working demo, logs, dashboard, or workflow
We want to see real implementation experience, not only a description of skills.
Preferred Stack
• Python
• FastAPI
• PostgreSQL
• Docker
• OpenAI / Anthropic / similar LLM APIs
Scope Note
This is a focused MVP build. The expected result is a working technical foundation, not a complete enterprise production system. Further work may include monitoring, alerts, authentication, queueing, deployment, admin dashboard improvements, and multi-agent workflow expansion.
The goal is not to build a full enterprise agent platform, but to create a clean working structure that can be extended safely in later phases.
Exact Deliverables
1. Working AI agent API endpoint
o Python backend, preferably FastAPI
o API-first structure
o Accepts user input and returns an LLM-generated response
o Clear request/response format
2. Basic LLM orchestration
o OpenAI, Anthropic, or similar LLM API integration
o Configurable system prompt / instruction layer
o Simple agent workflow logic
o Basic tool/function structure if required
3. PostgreSQL logging
o Store request, response, timestamp, status, and errors
o Simple database schema
o Setup or migration script included
4. Retry and error handling
o Basic retry logic for failed LLM/API calls
o Timeout handling
o Clear API error responses
5. Dockerized setup
o Dockerfile
o docker-compose setup for backend + PostgreSQL
o Environment variable configuration
o Local run instructions
6. Simple admin/status view or admin API
o Basic endpoint or minimal page to review recent agent runs
o Shows logs, statuses, and errors
o No complex dashboard required
7. Handoff documentation
o Local setup instructions
o API usage example
o Database setup steps
o Notes on next-phase improvements
Application Requirement
Please do not apply with only a written explanation.
When applying, include at least one of the following:
• Live demo URL of a previous AI agent, LLM backend, RAG, or automation system
• Short Loom/video walkthrough of a relevant project
• GitHub repo or code sample showing agent/API/backend structure
• System architecture diagram from a similar project
• Screenshots of a working demo, logs, dashboard, or workflow
We want to see real implementation experience, not only a description of skills.
Preferred Stack
• Python
• FastAPI
• PostgreSQL
• Docker
• OpenAI / Anthropic / similar LLM APIs
Scope Note
This is a focused MVP build. The expected result is a working technical foundation, not a complete enterprise production system. Further work may include monitoring, alerts, authentication, queueing, deployment, admin dashboard improvements, and multi-agent workflow expansion.