Agentic loop planner-executor for reliable multi-file edits BC-964

Project type: Innovation
Desired discipline(s): Engineering - computer / electrical, Engineering, Computer science, Mathematical Sciences, Mathematics
Company: Farpoint Technologies Inc.
Project Length: 6 months to 1 year
Preferred start date: 09/01/2025
Language requirement: English
Location(s): Vancouver, BC, Canada
No. of positions: 2
Desired education level: Master'sPhD
Open to applicants registered at an institution outside of Canada: No

About the company: 

Farpoint Technologies is a leading AI digital transformation consulting company that empowers top-tier organizations to build the AI-assisted workforce of the future. We specialize in providing consulting services to large public, private, and government entities, helping them create AI-accelerated workflows and innovative solutions. Our expertise includes leveraging LLMs, diffusion models, multimodal models, and executing special projects.

Describe the project.: 

This project develops an agentic loop that delivers non-deterministic but reliable, multi-file code edits through structured collaboration between a large model and one or more smaller models. Unlike single-model approaches, this system clearly delineates planning and execution phases via detailed JSON-based task specifications, ensuring improved consistency, clarity, and reliability in automated software development workflows. The innovation significantly reduces ambiguities, enabling precise and parallelized multi-file transformations while ensuring accurate, reproducible code modifications.

Main tasks for the candidate:
• Define comprehensive JSON schemas for specifying and communicating multi-file code transformations.
• Implement an asynchronous dispatcher distributing file-specific instructions to multiple parallel executor models.
• Develop a structured feedback mechanism allowing executors to request clarifications from the planner.
• Create a conflict-free merge module ensuring deterministic integration of concurrent edits.
• Benchmark the system's reproducibility and reliability on established code-generation tasks.
• Stretch goal: New record on SWE-Verified

Methodology / techniques:
• JSON schema definition and evaluation.
• Asynchronous task orchestration and parallel processing.
• Structured executor-planner feedback communication protocols.
• Conflict resolution leveraging CRDT-inspired merge strategies.
• Reproducibility benchmarking and variance analysis using SWE-bench.

Required expertise/skills: 

Required expertise/skills:
• Expertise in defining and managing structured JSON schemas.
• Familiarity with large language model API integrations and streaming completions.
• Understanding of Git diff management, parsing, and patch application.
• Basic knowledge of CRDT-based conflict-resolution techniques.
• Ability to script reproducible benchmarks (HumanEval, MBPP, or equivalent evaluation suites).

Assets (optional):
• Experience in concurrent task orchestration systems
• Familiarity with commit-signing and auditing best practices.