Introduction: The First Step is Visibility
Welcome to Part 1 of the Enterprise AI Observability and Optimization series.
In modern software engineering, AI tools have transformed how developers write code, debug issues, and refactor applications. Engineering teams use diverse tools across CLI agents (Gemini CLI, Antigravity, Claude Code, Cursor, Codex), local API proxies, and browser assistants.
However, before an organization can optimize costs or enforce security policies, it must achieve universal visibility. You cannot govern what you cannot observe.
This article explores how to capture developer AI interactions across every execution surface with zero friction and zero code modifications.
The Capture Challenge: Ephemeral Developer Workflows
Standard API proxies or web analytics tools fail to capture CLI coding assistant sessions because they only inspect HTTP headers. They hit three major blindspots:
-
Terminal Tool Executions: API proxies miss shell command outputs (
stdout/stderr), tool exit codes, and local environment execution context. - File Mutation & Git Diffs: Standard loggers see unformatted text strings. They cannot reconstruct structured line-by-line file diffs showing what code actually changed.
- Multi-Tool Fragmentation: Developers switch between CLI agents, IDEs, and browser tabs, scattering session history across unindexed silos.
Architecture of Tool-Agnostic Capture (rclm-hooks)
To solve this, ReclaimLLM (RCLM) approaches capture from inside the developer execution environment:
┌─────────────────────────────────────────────────────────────────────────┐
│ TOOL-AGNOSTIC CAPTURE ARCHITECTURE │
│ │
│ [Gemini CLI / Antigravity / Claude Code / Cursor / Codex / LiteLLM] │
│ │ │
│ (Native Event Hooks) │
│ ▼ │
│ ┌───────────────────────────────────────────────────────────────────┐ │
│ │ 1. LOCAL EVENT INTERCEPTION (Pre-execution prompts & tool inputs) │ │
│ └────────────────────────────────┬──────────────────────────────────┘ │
│ ▼ │
│ ┌───────────────────────────────────────────────────────────────────┐ │
│ │ 2. FILE DIFF & SHELL LOGGING (Structured git diffs & stdout) │ │
│ └────────────────────────────────┬──────────────────────────────────┘ │
│ ▼ │
│ ┌───────────────────────────────────────────────────────────────────┐ │
│ │ 3. UNIFIED TIMELINE INDEXING (Normalized provider-neutral format) │ │
│ └───────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────┘
1. Zero-Code Native Hooks
Installing native hooks requires two terminal commands:
pip install rclm && rclm-hooks-install
These hooks attach directly to local CLI agent runtimes without modifying application source code. They intercept lifecycle events in real time:
- Pre-Execution: Records user prompts and initial tool parameters.
- Post-Execution: Records paired tool execution outputs, terminal logs, and step-by-step file modifications.
2. Multi-Surface Coverage
ReclaimLLM unifies four capture paths into a single searchable timeline:
- Native CLI Hooks: Supports Gemini CLI, Antigravity, Claude Code, Cursor, and Codex.
- Local API Proxy: Intercepts traffic from LiteLLM proxy and custom scripts without code changes.
- Browser Extension: Logs web-based AI assistant interactions alongside CLI sessions.
-
Historical Backfill (
rclm-sync): Discovers and backfills sessions that predated hook installation.
Session Replay & Tribal Knowledge Preservation
Once captured, sessions are normalized into a searchable, provider-neutral format. Developers and managers can:
- Search Past Work: Use hybrid semantic and keyword search to locate debugging solutions, regexes, and architectural patterns from weeks ago in seconds.
- Replay Step-by-Step Diffs: Review paired tool calls and git file diffs generated during any AI session.
- Expiring Links for Code Reviews: Share email-bound, expiring links to captured sessions during pull requests, providing reviewers with full technical context.
Coming Up Next in Part 2
Capturing developer AI interactions is only the first step. In Part 2: Analyzing AI Usage, Attribution & Workflow Friction, we explore how engineering leaders use org-wide analytics, cost attribution, and RCLM Signals to detect developer workflow waste and model mismatch.
- Read Part 2: Analyzing AI Usage, Attribution & Workflow Friction
- Learn more at: reclaimllm.com
