How to Build a Devin AI Workspace Dashboard Setup

Monitoring a fleet of autonomous coding agents across multiple active repositories quickly devolves into a game of terminal tab roulette. Without a centralized surface to aggregate logs, run status, and compute metrics, engineering teams have no clear visibility into which agent tasks are blocked, which have succeeded, and how many credits are being consumed. Terminal logs and standard output feeds work well for a single developer watching a single run, but they fail to scale when a team coordinates dozens of concurrent agent sessions. A workspace dashboard for Devin AI aggregates active runs, pending code approvals, and task queues into a single visual control surface.

Rather than trying to build a custom dashboard application or database infrastructure from scratch, engineering teams can use an intelligent workspace as the telemetry layer. Fast.io serves as this shared substrate where agents and human developers collaborate. When Devin AI executes tasks, it generates extensive execution logs, JSON summary metrics, and directory outputs. By persisting these files to a shared Fast.io workspace, the storage layer becomes the central source of truth.

Using an intelligent workspace instead of local storage or commodity cloud drives ensures that files are automatically indexed for team discovery. Instead of hunting through terminal sessions, engineers can view run files side by side, search them semantically, and track changes in version history. This centralized approach bridges the gap between raw command-line executions and team-wide operations, ensuring that autonomous work remains observable and auditable.

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To feed the dashboard, the agent must export its execution logs and telemetry files programmatically. Devin AI provides an Analytics API (v2) to programmatically query session details and billing statistics using service tokens. This API returns hourly-aggregated metric sets, including credit or Agent Compute Unit (ACU) consumption and active user stats.

Because these metrics are hourly-aggregated rather than real-time, the endpoints are designed for periodic reporting and bulk data exports. Devin enforces a rate limit of 10 requests per hour per team on these analytics endpoints. If your monitoring script exceeds this frequency, the server returns a 429 Too Many Requests status code with a Retry-After header.

To work within these rate limits, the recommended workflow is to set up a script that runs once every thirty minutes. This script authenticates using a Service User API key (which carries a cog_ prefix) passed in the Authorization header as a Bearer token. The script fetches the latest session summaries from Devin, structures them into a standard JSON payload, and uploads them to the Fast.io workspace using the Fast.io REST API or MCP tools. You can read more about Fast.io's agent onboarding and capabilities in the agent onboarding documentation and the Fast.io pricing page.

Here is a Python script illustrating how to query Devin's session analytics and upload the JSON metrics directly to your Fast.io workspace:

import os
import requests

DEVIN_API_KEY = os.getenv("DEVIN_API_KEY")
DEVIN_ORG_ID = os.getenv("DEVIN_ORG_ID")
FASTIO_API_KEY = os.getenv("FASTIO_API_KEY")
WORKSPACE_ID = os.getenv("FASTIO_WORKSPACE_ID")

devin_url = "https://api.devin.ai/v3/enterprise/consumption/daily/organizations"
headers = {
    "Authorization": f"Bearer {DEVIN_API_KEY}",
    "X-Org-Id": DEVIN_ORG_ID,
    "Content-Type": "application/json"
}

response = requests.get(devin_url, headers=headers)
if response.status_code == 200:
    metrics_data = response.json()
    fastio_url = f"https://api.fast.io/current/workspace/{WORKSPACE_ID}/storage/addfile/"
    fastio_headers = {
        "Authorization": f"Bearer {FASTIO_API_KEY}"
    }
    files = {
        "file": ("devin_run_telemetry.json", str(metrics_data), "application/json")
    }
    upload_response = requests.post(fastio_url, headers=fastio_headers, files=files)
    if upload_response.status_code == 200:
        print("Telemetry successfully saved to Fast.io workspace.")

By directing Devin or a coordinator script to upload JSON summaries to the workspace, you establish an automated pipeline. Each run generates a standardized document, creating a historical record that forms the foundation of your visual dashboard.

Once the telemetry files and run logs land in the Fast.io workspace, the next step is transforming this raw file storage into a visual dashboard. Fast.io achieves this through Metadata Views. Metadata Views turn documents into a live, queryable database. Users describe the fields they want extracted in natural language, and Fast.io designs a typed schema, matches files in the workspace, and populates a sortable, filterable spreadsheet.

Unlike manual spreadsheet tracking or complex OCR parsing rules, Metadata Views process incoming PDFs, text logs, and JSON reports automatically. You configure a single view on the Devin runs folder by adding columns that represent your primary monitoring metrics. For a Devin run dashboard, you define columns like the following:

• Session ID. Text field extracting the unique Devin identifier.
• Run Status. Text field indicating if the run is completed, failed, or pending.
• Credits Consumed. Decimal field tracking the ACU cost of the run.
• Error Count. Integer field listing the count of exceptions or tracebacks.
• Completed Tasks. Integer field counting successful sub-goals.
• Execution Date. Date and Time field extracting when the session ran.

Fast.io supports 7 field types: Text, Integer, Decimal, Boolean, URL, JSON, and Date & Time. Because these extractions are incremental, you can add new columns to your view at any time without reprocessing existing files. This allows engineering leads to adjust their dashboard metrics dynamically as they refine their monitoring needs.

Furthermore, because the Fast.io MCP server exposes Metadata Views programmatically, other agents in your pipeline can query the spreadsheet grid. An agent can read the current status of all runs or trigger extraction when a new log file is uploaded. This enables automated oversight where agents monitor other agents, alerting human developers only when key metrics exceed predefined thresholds.

Centralize your autonomous agent logs in one intelligent workspace

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While Metadata Views provide a structured, spreadsheet-like view of Devin runs, teams often need to conduct deeper ad-hoc investigations. For instance, when a build fails or a credit usage spike occurs, drilling down into the exact root cause requires searching through hundreds of lines of text logs. Fast.io resolves this through Intelligence Mode.

When Intelligence is enabled on a workspace, files are automatically indexed for semantic search, summarization, and citation-backed chat. This built-in Retrieval-Augmented Generation (RAG) system allows developers to ask natural language questions about the entire history of Devin runs. Instead of downloading logs and running local grep commands, you query the workspace directly.

For example, an engineer can ask the portal or workspace assistant: "Which Devin run failed due to a missing environment variable yesterday?" The assistant processes the query using hybrid search, which combines exact full-text matching with semantic meaning retrieval. The search scans both filenames and file contents, returning a natural-language answer along with clickable citations pointing back to the specific line ranges in the source logs.

Developers can also configure the Fast.io MCP server to access this intelligence layer. An autonomous manager agent can programmatically query the workspace to check if a specific error pattern has occurred in prior runs. Refer to the Fast.io MCP server documentation for more details.

A critical phase of any autonomous coding project is the handoff from agent to human. An agent may write the codebase and complete its assigned goals, but a human engineer must ultimately review the changes, run integration tests, and take ownership. Fast.io supports this coordination through Collaborative Notes and ownership transfer features.

Collaborative Notes bring real-time, Google-Docs-style co-editing to the workspace, featuring live multiplayer cursors. People and agents participate as first-class co-editors. When Devin completes a run, it can open a shared note to document its changes, list the files modified, and outline open questions. The human engineer sees Devin's cursor moving in real-time as it drafts the summary, making the handoff highly collaborative.

If Devin requires feedback or runs out of execution credits, it can initiate an ownership transfer. An agent account can transfer an organization to a human via a claim link. The human developer steps in, enters a credit card to activate the 14-day free trial, and takes over the organization. Plans are structured as: Starter at $29 per month | Business at $99 per month | Growth at $299 per month, which require an organization subscription.

Once the human takes ownership, the agent retains admin access under the human's supervision. Every modification, download, and permission change is recorded in Fast.io's append-only audit log, ensuring that the entire lifecycle of the autonomous codebase remains fully transparent and auditable.