--- title: "Database Agent By Futuresmart AI" type: "AI Tool" url: "https://aidemos.com/tools/futuresmart-ai-database-agent" description: "We asked Database Agent to turn plain-English prompts into SQL and tables, with per-query latency, cost, and token stats. Relative dates often failed." category: "developer-tools" website: "https://agent.futuresmart.ai/database-agent" published: "2026-08-27T14:08:51.037697+00:00" updated: "2026-08-29T09:22:22.749700+00:00" evidenceCount: 32 verifiedCount: 25 coverage: "dense" --- # Database Agent By Futuresmart AI Plain-English database answers with visible SQL and unusually strong query analytics, best for well-formed questions. ## TL;DR Verdict **Strong on self-contained questions, weaker on relative-time comparisons** **Where it wins:** - You want non-technical users to ask plain-English questions against a live database and get readable tables or narrative answers. - You want every answer to expose the executed SQL for auditability. - You want follow-up questions to preserve conversational context across turns. **Main limitation:** You need reliable last-N-days or month-over-month comparisons when the relevant window may be empty. **Pricing:** Free $0 · Custom Custom `Live database Q&A` · `SQL shown` · `Auto charts` · `Per-query analytics` **Website:** [Visit Database Agent By Futuresmart AI](https://agent.futuresmart.ai/database-agent) ## Evidence (first-party, tested) *32 tested cells · 25/32 artifact-verified. Cite a cell by its Evidence ID, e.g. `ev:futuresmart-ai-database-agent·best-customers-with-unpaid-order-and-payment-method-follow-ups·ambiguity-handling`.* | Criterion | Scenario | Verdict | Proof | Evidence ID | | --- | --- | --- | --- | --- | | Ambiguity Handling | Best customers with unpaid-order and payment-method follow-ups | ◐ mixed | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/9f6a37e2aa8044c5a801b7b0c1fe9ef9.png?v=1) | `ev:futuresmart-ai-database-agent·best-customers-with-unpaid-order-and-payment-method-follow-ups·ambiguity-handling` | | Ambiguity Handling | Order pipeline breakdown with paid-pending edge case and last-month comparison | ⚠ struggled | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/dab3ae17cdd7418e93f0d3a34a2c765d.png?v=1) | `ev:futuresmart-ai-database-agent·order-pipeline-breakdown-with-paid-pending-edge-case-and-last-month-comparison·ambiguity-handling` | | Ambiguity Handling | cross-scenario | ⚠ struggled | 👁 observed | `ev:futuresmart-ai-database-agent·cross·ambiguity-handling` | | Business Insight | Best customers with unpaid-order and payment-method follow-ups | ✓ worked | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/9f6a37e2aa8044c5a801b7b0c1fe9ef9.png?v=1) | `ev:futuresmart-ai-database-agent·best-customers-with-unpaid-order-and-payment-method-follow-ups·business-insight` | | Business Insight | Order pipeline breakdown with paid-pending edge case and last-month comparison | ✓ worked | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/6595a94cc0764fd19e2cbc29376937d0.png?v=1) | `ev:futuresmart-ai-database-agent·order-pipeline-breakdown-with-paid-pending-edge-case-and-last-month-comparison·business-insight` | | Business Insight | cross-scenario | ◐ mixed | 👁 observed | `ev:futuresmart-ai-database-agent·cross·business-insight` | | Business Insight | Customer acquisition in the last 90 days vs previous 90 days | ◐ mixed | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/6ac5d5713df44d3fa93f2f0d7a0aa3fe.png?v=1) | `ev:futuresmart-ai-database-agent·customer-acquisition-in-the-last-90-days-vs-previous-90-days·business-insight` | | Chart / Visualization Support | Best customers with unpaid-order and payment-method follow-ups | ✓ worked | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/5ba48f6d9f66456a843a06ffa040331e.png?v=1) | `ev:futuresmart-ai-database-agent·best-customers-with-unpaid-order-and-payment-method-follow-ups·chart-visualization-support` | | Chart / Visualization Support | Order pipeline breakdown with paid-pending edge case and last-month comparison | ✓ worked | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/8d21d50f259d4b15b50484aa1650a5b2.png?v=1) | `ev:futuresmart-ai-database-agent·order-pipeline-breakdown-with-paid-pending-edge-case-and-last-month-comparison·chart-visualization-support` | | Chart / Visualization Support | cross-scenario | ◐ mixed | 👁 observed | `ev:futuresmart-ai-database-agent·cross·chart-visualization-support` | | Chart / Visualization Support | Customer acquisition in the last 90 days vs previous 90 days | ◐ mixed | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/6ac5d5713df44d3fa93f2f0d7a0aa3fe.png?v=1) | `ev:futuresmart-ai-database-agent·customer-acquisition-in-the-last-90-days-vs-previous-90-days·chart-visualization-support` | | Dashboard Workflow | cross-scenario | ✓ worked | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/b106ea9a1188473b853ddba5342171fc.mp4?v=1) | `ev:futuresmart-ai-database-agent·cross·dashboard-workflow` | | Export / Reuse | Best customers with unpaid-order and payment-method follow-ups | ✓ worked | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/a3d398759fbc4b7da58f48e4051a8c2a.png?v=1) | `ev:futuresmart-ai-database-agent·best-customers-with-unpaid-order-and-payment-method-follow-ups·export-reuse` | | Export / Reuse | cross-scenario | ✓ worked | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/a3d398759fbc4b7da58f48e4051a8c2a.png?v=1) | `ev:futuresmart-ai-database-agent·cross·export-reuse` | | Follow-Up Context | Order pipeline breakdown with paid-pending edge case and last-month comparison | ⚠ struggled | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/dab3ae17cdd7418e93f0d3a34a2c765d.png?v=1) | `ev:futuresmart-ai-database-agent·order-pipeline-breakdown-with-paid-pending-edge-case-and-last-month-comparison·follow-up-context` | | Follow-Up Context | Best customers with unpaid-order and payment-method follow-ups | ✓ worked | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/7c00dceab6d3435e8be7cde11f43f1be.png?v=1) | `ev:futuresmart-ai-database-agent·best-customers-with-unpaid-order-and-payment-method-follow-ups·follow-up-context` | | Follow-Up Context | cross-scenario | ◐ mixed | 👁 observed | `ev:futuresmart-ai-database-agent·cross·follow-up-context` | | FS Learning Value | cross-scenario | ✓ worked | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/5dac5c0133ed4f65b3e64cc8ab2ca642.png?v=1) | `ev:futuresmart-ai-database-agent·cross·fs-learning-value` | | Plain English Query Handling | Best customers with unpaid-order and payment-method follow-ups | ✓ worked | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/9f6a37e2aa8044c5a801b7b0c1fe9ef9.png?v=1) | `ev:futuresmart-ai-database-agent·best-customers-with-unpaid-order-and-payment-method-follow-ups·plain-english-query-handling` | | Plain English Query Handling | Order pipeline breakdown with paid-pending edge case and last-month comparison | ✓ worked | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/966189ab0285496cbc1ad56fa621a3e8.png?v=1) | `ev:futuresmart-ai-database-agent·order-pipeline-breakdown-with-paid-pending-edge-case-and-last-month-comparison·plain-english-query-handling` | | Plain English Query Handling | Customer acquisition in the last 90 days vs previous 90 days | ◐ mixed | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/6ac5d5713df44d3fa93f2f0d7a0aa3fe.png?v=1) | `ev:futuresmart-ai-database-agent·customer-acquisition-in-the-last-90-days-vs-previous-90-days·plain-english-query-handling` | | Plain English Query Handling | cross-scenario | ◐ mixed | 👁 observed | `ev:futuresmart-ai-database-agent·cross·plain-english-query-handling` | | Result Readability | Best customers with unpaid-order and payment-method follow-ups | ✗ failed | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/a7df6cf0481642a4b37828421e617f13.png?v=1) | `ev:futuresmart-ai-database-agent·best-customers-with-unpaid-order-and-payment-method-follow-ups·result-readability` | | Result Readability | Customer acquisition in the last 90 days vs previous 90 days | ✓ worked | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/6ac5d5713df44d3fa93f2f0d7a0aa3fe.png?v=1) | `ev:futuresmart-ai-database-agent·customer-acquisition-in-the-last-90-days-vs-previous-90-days·result-readability` | | Result Readability | Order pipeline breakdown with paid-pending edge case and last-month comparison | ◐ mixed | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/40f03c21d0b4461d8a19d2fdaa8dcb5e.png?v=1) | `ev:futuresmart-ai-database-agent·order-pipeline-breakdown-with-paid-pending-edge-case-and-last-month-comparison·result-readability` | | Result Readability | cross-scenario | ◐ mixed | 👁 observed | `ev:futuresmart-ai-database-agent·cross·result-readability` | | SQL Generation | Best customers with unpaid-order and payment-method follow-ups | ✓ worked | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/f70a13085b6b468c83d0816c24d18f84.png?v=1) | `ev:futuresmart-ai-database-agent·best-customers-with-unpaid-order-and-payment-method-follow-ups·sql-generation` | | SQL Generation | Order pipeline breakdown with paid-pending edge case and last-month comparison | ✓ worked | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/13cabebae8be4226acc533c2f6ee6b3f.png?v=1) | `ev:futuresmart-ai-database-agent·order-pipeline-breakdown-with-paid-pending-edge-case-and-last-month-comparison·sql-generation` | | SQL Generation | Customer acquisition in the last 90 days vs previous 90 days | ⚠ struggled | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/5dac5c0133ed4f65b3e64cc8ab2ca642.png?v=1) | `ev:futuresmart-ai-database-agent·customer-acquisition-in-the-last-90-days-vs-previous-90-days·sql-generation` | | SQL Generation | cross-scenario | ◐ mixed | 👁 observed | `ev:futuresmart-ai-database-agent·cross·sql-generation` | | SQL Visibility | Customer acquisition in the last 90 days vs previous 90 days | ✓ worked | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/5dac5c0133ed4f65b3e64cc8ab2ca642.png?v=1) | `ev:futuresmart-ai-database-agent·customer-acquisition-in-the-last-90-days-vs-previous-90-days·sql-visibility` | | SQL Visibility | cross-scenario | ✓ worked | 🧾 [proof](https://cdn.futuresmart.ai/public/aidemos/a8f72392970a4382b7b4ac7c8d661229.png?v=1) | `ev:futuresmart-ai-database-agent·cross·sql-visibility` | > 🧾 = artifact-verified (proof captured) · 👁 = observed (noted, no artifact) · verdicts: worked / mixed / struggled / failed. > **Strong on self-contained questions, weaker on relative-time comparisons** > > Database Agent is a strong fit when the question is self-contained: it returns readable tables and summaries, shows the executed SQL, keeps context across successful follow-ups, and uniquely logs latency, cost, and token usage per query. The main weakness is relative-date logic, where empty windows can collapse into no-answer responses or misdiagnosed failures instead of a usable comparison. Charting is also inconsistent on later turns, and some direct answers omit details the underlying data require. ## Demo Recording [Video: Database Agent By Futuresmart AI demo recording (download MP4)](https://cdn.futuresmart.ai/public/aidemos/bc0edcc2607040f8ac30d755188c48e7.mp4?v=1) [▶️ Watch (streaming)](https://stream.futuresmart.ai/embed/abc2fdf0-2211-4b80-b5ef-85f2e0d0458f) *Video — Walkthrough of the FutureSmart NL2SQL web app, including the landing page, database settings, and agent dashboard.* ## Feature-by-Feature Breakdown ### Natural-Language Database Querying Converts plain-English business questions into live database queries and returns readable tables, prose, and executed SQL. Exercised on customer rankings, orders-by-stage summaries, grouped questions, zero-result acquisition queries, and relative-date comparison prompts. **Input:** ``` Who are my best customers — the ones who order the most and spend the most? ``` **Output:** **Input:** ``` How many orders do we have at each stage right now? ``` **Output:** **Input:** ``` Compare the pending-but-paid orders to last month — has it improved or gotten worse? ``` **Output:** **Bottom line:** Strong on well-formed business questions that map cleanly to one live query. ### Contextual Follow-Up Querying Carries prior conversational context into later business questions so users can drill into the same customer set or pipeline breakdown without restating everything. Exercised on best-customers chains, order-pipeline turns, unpaid-order questions, payment-method and delivery breakdowns, and pending-but-paid edge cases. **Input:** ``` For the top 3 from that list — do any of them have unpaid orders? ``` **Output:** > **Image** **Input:** ``` What payment methods do these top 3 usually use? ``` **Output:** > **Image** **Input:** ``` Are there any orders that are pending but already paid? ``` **Input:** ``` Compare that to last month — same breakdown, I want to see if things have improved or got worse. ``` **Output:** > **Image** **Bottom line:** Context retention is strong across the successful follow-ups, but ambiguous references are guessed rather than clarified, and empty relative-time follow-ups can collapse into a no-answer response. ### Automatic Chart Generation Auto-renders charts for some query shapes, especially main summary queries such as customer rankings and order counts by status. The same capability was inconsistent on follow-up results, which were sometimes left as tables or text. **Input:** ``` Who are my best customers — the ones who order the most and spend the most? ``` **Output:** > **Image** **Input:** ``` How many orders do we have at each stage right now? ``` **Output:** > **Image** **Input:** ``` What percentage of our orders were successfully delivered vs cancelled? ``` **Output:** > **Image** **Input:** ``` What payment methods do these top 3 usually use? ``` **Output:** > **Image** **Bottom line:** Useful charting appears on the main queries, but follow-up chart-shaped results are inconsistent. ### SQL Inspection and Result Metadata Viewing Shows the generated SQL in a dedicated execution panel and exposes result data and chart metadata tabs so users can inspect what the tool ran and what rows came back. The SQL is shown raw rather than explained in plain language. **Input:** ``` Who are my best customers — the ones who order the most and spend the most? ``` **Output:** > **Image** **Input:** ``` Show all customers created in the last 90 days, how does new customer acquisition compare to the previous 90 days? ``` **Output:** > **Image** **Input:** ``` How many orders do we have at each stage right now? ``` **Output:** > **Image** **Bottom line:** A real transparency surface is built into the product, but the SQL is shown raw rather than explained in plain language. ### Query Analytics and Usage Observability Provides a dedicated analytics surface for reviewing past query runs, including latency, cost, token counts, query status, query text, chat/session IDs, filters, and log-level history. It surfaces per-query operational metrics and usage history in one place. **Input:** ``` Inspect the Analytics page for query-level cost, latency, token counts, and query logs. ``` **Output:** > **Image** **Bottom line:** This is the tool's most distinctive strength: it makes per-query cost, latency, and token usage visible in one place. ### Relative-Date Comparison Handling Answers relative-time comparison questions such as last-90-days and last-month prompts, but can be brittle when the relevant time window is empty. In those cases it may suppress an independently-computable comparison or give up instead of adapting to the dataset boundary. **Input:** ``` Show all customers created in the last 90 days, how does new customer acquisition compare to the previous 90 days? ``` **Output:** > **Image** **Input:** ``` Compare that to last month — same breakdown, I want to see if things have improved or got worse. ``` **Output:** > **Image** **Bottom line:** Relative-time questions are the clearest reliability gap: when the chosen window is empty, the tool often stops instead of adapting. ## Observed tiers The report says the tool was tested on the Free ($0) tier and that a Custom tier is also offered. | Plan | Price | Notes | | --- | --- | --- | | Free ★ (tested) | $0 | Self-serve tier used for the hands-on test. | | Custom | Custom | Bespoke configuration and integrations handled by FutureSmart's engineering team. | *Pricing details were taken from the report's testing setup note.* ## Is It Right For You? **Use it if** - You want non-technical users to ask plain-English questions against a live database and get readable tables or narrative answers. - You want every answer to expose the executed SQL for auditability. - You want follow-up questions to preserve conversational context across turns. - You care about per-query latency, cost, and token analytics in a dedicated dashboard. **Skip it if** - You need reliable last-N-days or month-over-month comparisons when the relevant window may be empty. - You need the tool to ask a clarifying question before resolving an ambiguous reference. - You need chart generation on every chart-shaped answer. - You need the SQL logic explained in plain language rather than shown raw. ## Classification - **Category:** developer-tools - **Subcategory:** other-developer-tools - **Type:** text - **Built for:** Other ## Frequently Asked Questions **Q: Does Database Agent query a live database without requiring SQL?** Yes. In the test, plain-English questions were sent to a live PostgreSQL database and the tool returned answers without the user writing SQL. **Q: Does it show the SQL it ran?** Yes. Every tested turn exposed an "Executed SQL" panel, and some turns also showed Result Data and Chart Metadata tabs. **Q: Can it keep context across follow-up questions?** Mostly yes. It successfully carried context through the best-customers chain and the first three order-pipeline turns, but it later guessed an ambiguous referent instead of asking for clarification. **Q: Does it always generate charts?** No. The main ranking and order-stage queries charted, but later chart-shaped follow-ups such as payment-method counts and the delivered-vs-cancelled split were left as tables or text. **Q: How does it handle last-90-days or last-month comparisons?** Poorly when the time window is empty. The last-90-days customer-acquisition question suppressed the prior-period comparison, and the month-over-month follow-up gave up instead of diagnosing the data cutoff and using the best available window. **Q: What does the Analytics page track?** It tracks per-query latency, cost, token counts, query status, and searchable query history. The report treats this as the product's standout operational feature. **Q: Is pricing published?** The report observed a Free ($0) tier used for testing and a Custom tier, but it did not capture a separate full pricing page. ## Similar Tools AI tools similar to Database Agent By Futuresmart AI: - [BlazeSQL](https://aidemos.com/tools/blazesql) — Plain-English database analytics with SQL transparency, auto-charts, and self-correcting follow-ups. - [Draxlr](https://aidemos.com/tools/draxlr) — AI Data Analyst · NL2SQL · Data Visualization · June 2026 - [AskYourDatabase](https://aidemos.com/tools/askyourdatabase) — AskYourDatabase Review: NL2SQL AI Database Chatbot Tested (2026) - [Basedash](https://aidemos.com/tools/basedash) — AI-Native BI · NL2SQL · Data Analyst Chat · June 2026 - [Definite](https://aidemos.com/tools/definite) — AI Data Platform · NL2SQL · AI Analyst · Dashboard Builder · June 2026 - [Querio](https://aidemos.com/tools/querio) — AI Data Analyst · NL2SQL · Data Visualization · June 2026 - [Dot](https://aidemos.com/tools/dot) — Plain-English database querying with strong SQL transparency and follow-up context, but uneven insight and data-recency handling. - [AI for Database](https://aidemos.com/tools/ai-for-database) — Plain-English live database querying with inline SQL, charts, follow-ups, and cost visibility. - [camelAI](https://aidemos.com/tools/camelai) — A self-checking AI data agent for live database Q&A, follow-ups, and charts on request. - [Anomaly AI](https://aidemos.com/tools/anomaly-ai) — A traceable AI data analyst that turns plain-English database questions into live answers, charts, and saved insights. ## Need a custom AI solution for this use case? If you are looking to build a custom database query assistant, SQL generation, or query analytics tool for your business or internal workflow, email us at [contact@futuresmart.ai](mailto:contact@futuresmart.ai). ### Found something inaccurate or missing? We try to keep our AI research accurate and useful. If you found outdated information, an issue, or have a suggestion, email us at [collaborate@aidemos.com](mailto:collaborate@aidemos.com).