Reddit post pitches "agent action platform" for API-to-agent bridging
u/bhayya6698 describes an internal layer that wraps business APIs into MCP tools and multi-step automations for AI agents, and asks the community how they handle auth, permissions, retries, and long-running workflows today.
Score breakdown
The post surfaces a concrete architectural challenge in production agentic systems — that raw business APIs require substantial wrapping infrastructure before agents can use them safely and reliably — and proposes a two-tier model (MCP tools vs. multi-step automations) as a potential solution pattern.
- 01Posted by u/bhayya6698 on r/AI_Agents as a product validation exercise based on an internal build.
- 02The stated problem: business APIs were built for apps and developers, not AI agents.
- 03Common agent actions cited include creating payment links, updating CRM records, creating support tickets, checking order status, and triggering refunds or approvals.
u/bhayya6698 posted to r/AI_Agents describing a product concept derived from an internal tool their team built to solve a recurring problem: AI agents can only be useful if they can take action, but most business systems expose APIs that were designed for human developers and traditional apps — not autonomous agents. Common agent tasks like creating payment links, updating CRM records, filing support tickets, checking order status, submitting and polling long-running jobs, and triggering refunds or approvals all require significant infrastructure beyond the raw API call itself: authentication, secrets management, user permissions, required input mapping, payload validation, retries, logging, failed-call diagnosis, approval rules, and long-running workflow handling.
The internal layer they built addresses this by converting APIs into two types of agent actions: **MCP tools** for discrete, single-call API actions, and **automations** for multi-step workflows.
The internal layer they built addresses this by converting APIs into two types of agent actions: **MCP tools** for discrete, single-call API actions, and **automations** for multi-step workflows. As a concrete example, `get_payment_status` is modeled as a tool, while the sequence of creating a payment link, polling for payment confirmation, and updating a CRM record is modeled as an automation. The broader concept is described as an "agent action platform" where agents trigger safe, controlled actions and the platform handles all surrounding concerns — auth, permissions, API calls, polling, retries, monitoring, and failure handling. The post closes by soliciting community input on current approaches and asking where integrations most commonly break first: auth, missing inputs, wrong tool selection, permissions, long-running jobs, or debugging.
Key facts
- 01Posted by u/bhayya6698 on r/AI_Agents as a product validation exercise based on an internal build.
- 02The stated problem: business APIs were built for apps and developers, not AI agents.
- 03Common agent actions cited include creating payment links, updating CRM records, creating support tickets, checking order status, and triggering refunds or approvals.
- 04Infrastructure required around each API call includes auth, secrets, permissions, input mapping, payload validation, retries, logs, failure diagnosis, approval rules, and long-running workflow handling.
- 05The internal solution splits API interactions into two types: MCP tools (single API actions) and automations (multi-step workflows).
- 06Example given: `get_payment_status` as a tool; create payment link + poll payment + update CRM as an automation.
- 07The post asks whether the community lets agents call APIs directly or wraps workflows behind controlled endpoints, and where failures most commonly occur.
Topics
Summary and scoring are generated automatically from the original article. We always link back to the publisher and never republish images or paywalled content. Last processed Jun 9, 2026 · 17:05 UTC. How this works →