How are you wiring AI agents into your real-world stack? Would love your feedback on Wysera

Most AI agents still live in demos and Twitter threads. They look magical in isolation, then fall apart the moment you try to plug them into real workflows, legacy systems, and team processes.

With Wysera, we're solving a very specific problem: How do you design, run, and iterate AI agents that actually talk to your tools, data, and APIs in production - without turning your stack into spaghetti?

What Wysera does (in plain English)

We're building an agent orchestration and execution layer that sits between your LLMs and your real stack: CRMs, EHRs, ticketing systems, analytics, internal APIs, and more.

Instead of hand-rolling yet another "LLM + tools" script, you define:

- The agents (roles, capabilities, tools)

- The graph/flow (who calls what, in which order, with what guardrails)

- The connectors (HTTP APIs, webhooks, queues, DBs, SaaS apps)

Wysera then runs the agents, tracks state, logs every decision, and lets you observe and iterate like you would with any serious piece of infrastructure.

Who this is for

- Engineers gluing LLMs into existing products, ops, and back-office workflows

- Teams experimenting with AI copilots or agents for ops, support, or sales

- Founders trying to move beyond chatbots into actual autonomous workflows

A few questions for you:

1. What's the most brittle part of your current AI-agent setup? (Tool calling? Context management? State between steps? 3rd-party integrations?)

2. If you already use something like LangGraph, Reworkd, or OpenAI flows — what still forces you back to custom code?

3. How autonomous do you actually want agents to be in production? (Suggest-only? One-click approval? Fully automated within guardrails?)

4. What would make you trust an agent platform enough to put it into a core workflow?

Drop a comment with your stack, one workflow you wish an agent could fully own, and the top reason you haven't shipped that agent yet. Happy to share more details or a behind-the-scenes look.

— Girish