The Limits of Prompting: ArchitectingTrustworthy Coding Agents

Prompt engineering has its limits. Learn how a multi-agent architecture, enriched with deep context, boosted our AI agent's suggestion acceptance rate from 12% to over 60%.

#1about 2 minutes

Prototyping a basic AI code review agent

A simple prototype using a GitHub webhook and a single LLM call reveals the potential for understanding code semantics beyond static analysis.

#2about 2 minutes

Iteratively improving prompts to handle edge cases

Simple prompts fail to consider developer comments or model knowledge cutoffs, requiring more detailed instructions to improve accuracy.

#3about 5 minutes

Establishing a robust benchmarking process for agents

A reliable benchmarking pipeline uses a large dataset, concurrent execution, and an LLM-as-a-judge (LLJ) to measure and track performance improvements.

#4about 2 minutes

Decomposing large tasks into specialized agents

To combat inconsistency and hallucinations, a single large task like code review is broken down into multiple smaller, specialized agents.

#5about 6 minutes

Leveraging codebase context for deeper insights

Moving beyond prompts, providing codebase context via vector similarity (RAG) and module dependency graphs (AST) unlocks high-quality, human-like feedback.

#6about 3 minutes

Introducing Awesome Reviewers for community standards

Awesome Reviewers is a collection of prompts derived from open-source projects that can be used to enforce team-specific coding standards.

#7about 1 minute

Key takeaways for building reliable LLM agents

The path to a reliable agent involves starting with a proof-of-concept, benchmarking rigorously, using prompt engineering for quick fixes, and investing in deep context.