Feat: Create Agent Efficiency Benchmark Suite

[marco0560]

Issue: Create Agent Efficiency Benchmark Suite

Summary

Create a benchmark suite that measures the impact of Codira on agent-assisted repository analysis and modification tasks.

The objective is to quantify reductions in:

• token consumption
• context retrieval volume
• tool calls
• task completion time

while maintaining equivalent task success rates.

Motivation

Traditional benchmarks focus on:

• indexing speed
• query speed
• database size

These metrics do not capture the value Codira provides to AI agents.

A more meaningful metric is:

tokens_to_successful_outcome

Examples:

• successful code modification
• successful architecture analysis
• successful symbol discovery
• successful dependency investigation

Benchmark Methodology

For each task:

Baseline Workflow

Agent uses:

• file reads
• grep
• find
• ripgrep
• repository exploration

without Codira assistance.

Codira Workflow

Agent may use:

• calls
• refs
• ctx
• audit
• embeddings
• architecture reports

before reading repository files.

The same:

• repository snapshot
• model
• prompt
• task definition

must be used for both workflows.

Metrics

Record:

repo
task
workflow
model
input_tokens
output_tokens
total_tokens
tool_calls
wall_time
success

Derived metrics:

token_savings_percent
tool_call_reduction_percent
time_reduction_percent

Repository Selection

Repositories should represent multiple scales and ecosystems.

Small

• Codira
• Fontshow
• Requests

Medium

• Redis
• fmt
• tree-sitter-c
• tree-sitter-python

Large

• PostgreSQL
• CPython
• LLVM

Selection criteria:

• active projects
• diverse languages
• varying architectural complexity
• reproducible local indexing

Task Selection

Tasks must be:

• deterministic
• repeatable
• objectively verifiable

Symbol Discovery

Example:

Find the definition of symbol X.
List all callers.

Impact Analysis

Example:

Determine all locations affected by a change to API Y.

Architecture Investigation

Example:

Identify all dependencies entering subsystem Z.

Bug Localization

Example:

Locate the implementation responsible for behavior Q.

Patch Preparation

Example:

Add a parameter to API X and identify all required call-site updates.

Documentation Generation

Example:

Produce an architecture summary of subsystem Z.

Deliverables

Phase 1

• Benchmark schema
• Repository manifests
• Task definitions

Phase 2

• Benchmark harness
• Token accounting
• Result storage

Phase 3

• Automated benchmark reports
• Historical trend tracking
• Documentation publication

Acceptance Criteria

• Benchmarks are reproducible.
• Task definitions are deterministic.
• Token counts are recorded consistently.
• Reports compare baseline and Codira-assisted workflows.
• Results can be published as part of release documentation.