# Agile Loop L.L.C.

**Canonical:** https://apis.io/providers/agile-loop-llc/  
**Website:** https://agileloop.ai  
**APIs profiled:** 0

Agile Loop L.L.C. is a London-founded enterprise AI company (founded 2023, backed by 500 Global) building Large Action Models — action-first foundation models trained on thousands of software interfaces so AI agents can operate any user interface and execute multi-step workflows end-to-end from natural-language instructions. Its product suite spans Sector Fusion (telecom network analytics), an Intelligent Evaluation Platform (AI scoring and fraud detection), LLM Ops (governance for LLM applications), an Aviation Ops Suite, and SAM-X (natural-language Excel automation), alongside the open-source Synapse Copilot toolkit for connecting LLMs to APIs and an embeddable Copilot widget for SaaS products. Agile Loop publishes no public OpenAPI, developer portal, or versioned client SDKs at the time of this profile.

## Kin Score — 10.9 / 100 (minimal)

Scored 2026-08-21 under rubric 0.12.0. Trend: flat (+0.0 from 10.9).

| Facet | Score |
|---|---|
| Discoverability | 50.0 |
| Contract Quality | 0.0 |
| Governance | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 18.4 |
| Developer Ergonomics | 7.1 |
| Commercial Clarity | 10.5 |
| Access Clarity | 10.5 |

## Agent readiness — 0.0 (human-only)

| Dimension | Value |
|---|---|
| Spec Presence | no |
| Agentic Access | no |
| Reversibility Documented | no |
| MCP Server | no |
| Auth Clarity | no |
| Idempotency | no |
| Error Semantics | no |
| OpenAPI Examples | no |
| Rate Limit Signal | no |
| Event Surface Described | no |
| Agent Skills | no |
| Well Known Catalog | no |
| Consent Identity | no |
| Agent Card | no |
| Dry Run Mode | no |

## Access

Unknown — onboarding: unknown, pricing: unknown, trial: no (confidence: low).

## Security (1)

- **Agile Loop Llc Domain Security** — TLSv1.3 · HSTS · DMARC

## Tags

Company, Artificial Intelligence, AI Agents, Large Action Models, LLM, Automation, Enterprise AI, Foundation Models

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Profiled by [API Evangelist](https://apievangelist.com) and published on [APIs.io](https://apis.io/providers/agile-loop-llc/). Scores are computed from the provider's own public artifacts under a published rubric.