How Elastra's Adoption Dashboard gives engineering leaders full visibility into every AI agent

Engineering organizations are adopting AI agents at an accelerating pace. GitHub Copilot, Claude, Cursor, Gemini CLI, Windsurf, Cline, Roo Code — different engineers on the same team use different tools, different models, and different workflows. The result is a fragmented landscape where AI is in use everywhere but understood nowhere.

This fragmentation creates a critical governance problem: organizations are spending money on LLM tokens, shaping engineering processes around AI agents, and making strategic bets on AI productivity — all without reliable data on what is actually happening.

How many agent executions happened last week? Which teams are using AI most intensively? Which models are consuming the most tokens? Which engineers are producing the most AI-assisted commits? Without answers to these questions, AI adoption is essentially a black box.

Elastra's Adoption Dashboard was built to close this gap. It provides a single, centralized view of AI agent activity across the entire organization — regardless of which tool each engineer uses.

The Elastra Adoption Dashboard is the command center for understanding how AI agents are being used across the organization. It is designed to be readable at a glance — surfacing the most important numbers at the top, then providing detailed breakdowns for teams that need to dig deeper.

The dashboard is organized into two major areas: AI agent activity (executions, tokens, model usage, skills, personas, users, teams, projects) and SCM activity (commits, pull requests, lines changed, reviews, daily trends by author). Together, they connect AI agent usage to concrete engineering output.

The dashboard opens with four headline metrics that give leadership an immediate reading of the organization's AI engagement.

Active Members shows how many engineers actively triggered at least one AI agent session in the selected period. This is the adoption rate in its purest form — not licenses purchased, not tools installed, but actual usage.

Agent Executions is the total count of AI agent sessions recorded. This is the raw volume of AI-assisted work happening across the organization. Combined with active members, it reveals the intensity of usage per person.

Tokens Consumed tracks the aggregate LLM token usage across all agent sessions. This is the cost driver. Organizations that understand token consumption by team, model, and skill can make informed decisions about LLM routing and budget allocation.

Lines Changed tracks the total lines of code added, modified, or deleted in commits associated with AI agent sessions. This metric bridges AI activity to engineering output — connecting agent usage to the actual repository changes that ship.

One of the most revealing charts in the dashboard is AI Usage by Agent Type. It shows the distribution of agent executions across every agent tool in use — GitHub Copilot, Claude Code, Cursor, Gemini CLI, Windsurf, Cline, Roo Code, Kimi, Amp, and others.

This chart answers the question that most organizations cannot answer today: which AI tools are engineers actually using? Not which ones are licensed, not which ones IT approved, but which ones are generating actual sessions against the Elastra MCP server.

The insight is frequently surprising. Organizations often discover that a tool adopted by only a few engineers accounts for a disproportionate share of executions — indicating high engagement from a small group. Or conversely, that widely licensed tools have low actual usage rates, which informs licensing renewal decisions.

Elastra supports this visibility across all 13 agent types it manages. Because all agents route through the same MCP server regardless of tool, the data is comparable and unified — something that is impossible to achieve by querying each tool's vendor analytics separately.

Beyond which agents are used, platform teams need to understand which LLM models are being used and at what cost. Elastra tracks executions by model (GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, and others) and tokens consumed by model, enabling a direct cost-per-model analysis.

The Avg Latency by Model chart adds a quality dimension: it shows the average response latency in milliseconds for each model. When combined with token cost data, this gives platform teams the inputs they need to make data-driven routing decisions — favoring faster, cheaper models for tasks where latency sensitivity is low.

Skill analytics add another layer. Elastra tracks which skills (reusable AI workflows defined by the organization) are being used most frequently and which are consuming the most tokens. This reveals whether the organization's investment in building skills is being realized in actual usage — and which skills may need revision or deprecation.

These three dimensions together — model, token cost, skill, latency — are what make it possible to treat LLM spend as a manageable engineering variable rather than an opaque infrastructure cost.

The adoption dashboard breaks down AI usage along three organizational dimensions: by user, by team, and by project. These breakdowns answer different questions at different levels of the organization.

AI Usage by User and Tokens by User reveal which engineers are the heaviest users of AI agents. This is not about surveillance — it is about understanding who the AI power users are, so that their practices can be understood, documented, and propagated to the rest of the organization.

Usage by Team shows whether AI adoption is uniform across the organization or concentrated in specific teams. Uneven adoption is a signal: it may indicate that some teams have clearer workflows for AI integration, have better access to training, or are working on problem types where AI is more effective.

Usage by Project connects agent activity to specific codebases. This is especially useful for organizations running many services in parallel — it shows which projects are receiving AI-assisted development attention and which are not, enabling more deliberate resource and tooling decisions.

Most AI observability tools stop at session counts and token usage. Elastra goes further by integrating with source control management (SCM) data to connect AI agent activity to actual repository output.

The SCM section of the Adoption Dashboard includes: Top Contributors by commits, Lines Changed by Author, PRs Opened by Author, Reviews by Author, Daily Commits trend, and Daily PRs trend. These metrics cover the full development cycle — from code creation to review to merge.

This integration allows organizations to answer a question that vendor analytics cannot: among engineers who are active AI users, what is their SCM output? Are the teams with the highest agent execution counts also the teams shipping the most commits and PRs? Or is there a gap between AI session volume and actual code delivery?

The answer to that question determines whether AI adoption is translating into engineering velocity — and, if it is not, where to investigate.

All metrics in the Adoption Dashboard are collected passively through the Elastra MCP server. Engineers do not need to log their sessions, tag their work, or fill out any form. Every time an agent calls an Elastra MCP tool — whether to retrieve context, write a memory, run a sync, or fetch rules — the execution is recorded.

This means the data reflects genuine behavior, not self-reported behavior. Engineers use their preferred tools. Elastra sees the data automatically because all tools route through the same MCP interface.

The knowledge-activity metrics (Knowledge Writes, Searches, Sync Runs) complement the execution data by showing how actively the organization is building and querying its shared context. An organization with high agent execution counts but low knowledge writes may be using AI effectively in sessions but not yet investing in the shared memory layer that makes AI agents progressively more accurate over time.

Transparency is a core design principle. The Adoption Dashboard is not hidden from engineering teams — it is shared with them. When engineers can see the same data that leadership sees, AI adoption becomes a collective engineering concern rather than a top-down mandate. Teams that understand their own usage patterns are better positioned to improve them.

The Adoption Dashboard is not a reporting tool. It is a decision-enabling tool. The distinction matters because the purpose of collecting this data is not to produce reports — it is to answer questions that currently cannot be answered, and to give engineering leaders the information they need to act.

Which LLM model is delivering the best cost-to-latency ratio for our use cases? The model analytics answer this. Which teams need more AI onboarding support? The team distribution reveals this. Are our investment in skills libraries paying off in actual usage? The skill analytics measure this. Is AI adoption translating into more commits and fewer review cycles? The SCM integration connects these dots.

Each of these questions was previously unanswerable without significant manual effort — or entirely unanswerable because the data did not exist. The Adoption Dashboard makes them routine queries against a live, unified data source.

This is what organizational AI maturity looks like: not the number of tools installed or the number of engineers with access, but the ability to observe, measure, and improve the way AI agents work within the organization — continuously, with data, at scale.