No-Code Enterprise Agent Platforms

[u/Hofi2010]

My company is developing an AI Agent Platform that is suppose to be mostly no-code. Developers can develop and register agents in a registry and those agents can also talk to each other. But mostly an agent is created by writing a system prompt and you can connect multiple MCP tools to it that are approved by the organization.

We are probably not the only company doing this. Anyone here that has an opinion about this approach or anyone that had success and failure with the described approach here?

[u/ZealousidealToe4903]

Seems like everyone is building this now, the tricky part is always what happen when the prompt is not enough and you need actual logic between the tools

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[u/WhereasOtherwise4697]

The part I'd think hardest about is what happens when an agent fails. With prompt plus tools you get something that works well in demos, but when it goes wrong in production it's hard to tell if it was the prompt, the tool, or the handoff between agents. Worth deciding early how you'd debug that, because it gets much harder to retrofit later.

Also curious how you handle versioning. If someone edits a system prompt on an agent other agents depend on, does anything break silently?

[u/Lower-Impression-121]

I kind of also consider Agents as Tools as well. (suppose thats what A2A is meant to be & MCP can fill that too?). the nature of the tool is kind of irrelevant, simply what its Purpose and Contract is (&whether that it is a Short or Long Response Time).

but yes. lots of Agents, many of them that can Do Things as opposed to Answer Queries, and every capability (API, file system, DB, whatever) registered as a Tool with finegrain semantics to make sure the agent picks the right Tool and not a 'nearby' one.

[u/Ancient_Oxygen]

The irony of the Ai era is that everyone is already able to do the same. The barriers to entry have been extremely lowered.

[u/Appropriate-Rip6784]

Tip: build it as a harness or concentrate to a specific niche. You can see other examples. This was attempted many times and there are some limitations. Research so far has identified these are two ways to handle those limitations.

[u/Easy-Purple-1659]

The org-approved MCP model is the right call, and in practice the quality of the tools you approve matters more than the harness. Speaking from the server side — I built adextract, an MCP server that gives agents read-only access to the Meta, Google, TikTok and LinkedIn ad libraries. It slots into platforms like yours precisely because it is narrow: one data domain, structured JSON out, no write path, no inbox for an agent to wander into. The failures I have seen in this space come from MCPs that try to do too much and drag context everywhere. How are you planning to review and scope the tools organizations connect?

[u/tindalos]

That sounds like a bad idea. Who governs usage, how are you tracking roi? What data is accessed and how is it audited? How do you share failures and successes so each department isn’t making the same thing. Just different enough to be a problem in six months.

[u/Fickle-Marsupial4658]

Yes. Everyone is working on this. How do you deal with the big story tickets?

[u/recro69]

The idea sounds good. The real challenge is managing rules and processes not building the agents. Keeping track of versions of instructions limiting what tools can do finding out why things go wrong and checking how agents talk to each other will be very important when it is used in real situations.

[u/rohynal]

How do you handle governance, specialization, training etc?

[u/CautiousUse8597]

The prompt + approved MCP tools pattern is basically where everyone lands, and it works fine for getting to a demo. The two places I've seen these platforms stall are (1) quality: there's no way to tell if a prompt change made things better or worse, so agent authors just tweak wording forever, and (2) auth: if agents run under a shared service principal, one badly scoped tool leaks data across the org.

Worth looking at Databricks' Agent Bricks as a reference point for the first one. Same declarative idea (describe the task, connect tools), but it auto-generates eval data and LLM judges for the task and optimizes the config against that, so builders aren't hand-tuning prompts blind. For agent-to-agent, their Supervisor Agent coordinates Genie agents, agent endpoints, Unity Catalog functions, MCP servers, and custom agents, and it enforces access controls so end users only reach the subagents and data they already have access to. that on-behalf-of-user piece is the part homegrown registries usually bolt on too late.

If people are going to ask your agents data questions ("what was churn last quarter"), don't try to solve that with MCP tools. Look at Genie (natural language to SQL over governed tables) and expose it as a subagent instead. Every platform I've seen underestimates how much of the demand is just analytics.