Falcon AI
An AI copilot embedded in the Future AGI dashboard that handles platform tasks, runs analysis, and answers questions through natural language.
About
Falcon AI is a copilot built into the Future AGI dashboard. It has access to over 300 platform tools and can work across datasets, evaluations, traces, experiments, prompts, and admin settings through natural language. It knows what page you are on, what entity you are looking at, and acts on that context directly.
You describe a task, Falcon AI executes it. You ask a follow-up, it goes deeper. A single conversation can span multiple features: start from an evaluation regression, drill into the failing traces, inspect the dataset behind them, and compare against a different model.
What Falcon AI can do
Analyze. Ask questions about your data and get quantified answers, not summaries.
“Which eval metrics dropped this week compared to last week?” “What’s the p95 latency for the summarization endpoint?” “Show me a cost breakdown by model for the last 30 days.”
Create. Build platform entities without leaving the chat.
“Create a dataset called qa-golden with columns for query, expected_answer, and context.” “Run faithfulness and hallucination evals on the customer-support dataset.” “Set up an A/B experiment comparing GPT-4o and Claude Sonnet on the QA dataset.”
Debug. Search traces, drill into spans, correlate across features.
“Show me traces with timeout errors from the last 24 hours.” “Find traces where the model hallucinated and show me what context was retrieved.”
Chain. Work across features in a single conversation. Each follow-up builds on the previous result.
“The faithfulness score on run 12 dropped. Show me the failing traces, then compare the prompts used in run 11 vs run 12.”
Key capabilities
| Capability | Details |
|---|---|
| Page-aware context | Automatically detects the current dashboard page and entity. Ask “why is this score low?” and it knows which evaluation you mean. |
| 300+ tools | Covers datasets, evaluations, traces, experiments, prompts, agents, simulations, cost analytics, and admin settings. |
| Multi-step execution | Chains up to 50 tool calls per turn. Runs independent calls in parallel, sequential calls in order. |
| Skills | Pre-built and custom slash commands that package multi-step workflows. Type / to access them. |
| File and URL input | Upload PDFs, CSVs, images, or paste URLs. Falcon AI extracts content and uses it as context. |
| MCP Connectors | Connect external services (Linear, Slack, GitHub, Sentry) so actions like “create a ticket for this regression” work in chat. |
Falcon AI vs MCP Server
Future AGI has two AI interfaces for different contexts:
| Falcon AI | MCP Server | |
|---|---|---|
| Where | Inside the dashboard (browser) | Inside your IDE (Cursor, Claude Code, VS Code) |
| Who | Platform users browsing the dashboard | Developers writing code |
| Context | Knows what page is open, what entity is being viewed | Knows the codebase and files being edited |
| Output | Rich rendering: charts, tables, completion cards | Text-only responses |
Both share the same tool layer.