What makes an AI agent production-ready?

Grounding on real data, narrow scope, tool calls with auth and retries, traces and evals on every run, and explicit guardrails — rate limits, allowlists, approval gates, kill switches. If any of those are missing, the agent is a demo.

Do you build with the AI SDK, LangChain, or something else?

We default to the Vercel AI SDK and provider-native tool calling because they are the cleanest way to ship in production today. We will use other frameworks where a project genuinely needs them, not because they are trendy.

Can the agent run fully autonomously?

Eventually, in narrow categories, once evals show it earns the trust. Day one, every consequential action sits behind a human approval. We are not in the business of letting an open-loop agent loose on a customer's systems.

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AI agent development

AI agents built to run in production, not in a tweet.

Agents that take action — book meetings, file tickets, run a research loop, qualify a lead, draft a proposal. Built grounded, observable, and safe, with humans in the loop where it matters.

The problem

Agents demo great. They ship rarely.

Most agent demos work because the demo is the test. In production, agents face messy data, broken APIs, unclear instructions, and adversarial users. Without grounding, observability, and proper guardrails, they are a liability — fast.

Open-loop agents that hallucinate actions on real systems
No tracing, no eval, no way to know why a run failed
Tool stacks duct-taped together with no error handling
Cost and latency that spike the moment usage is real

What Ideaplexa does

How we build agents

We build agents the way we build any production system — with specs, tests, observability, and guardrails. We use the modern AI SDK and tool-calling primitives, ground agents on your real data, and put humans in the loop on actions that matter.

Use cases

Where this shows up in real businesses

Sales research and outbound

An agent that researches accounts, drafts personalized outreach, and lets a human approve before sending.

Support resolution

An agent that drafts responses, runs simple actions (refunds, password resets), and escalates the rest.

Internal ops agents

Agents that run recurring ops jobs — onboarding new vendors, generating weekly reports, syncing tools.

Vertical research agents

Domain-specific research agents for legal, finance, recruiting, or competitive intelligence.

How we work

The process, step by step

Step 01
Define the job
What the agent does, what it does not do, what 'good' looks like, and what failure modes are unacceptable.
Step 02
Build tools and grounding
We wire up the tools, the data sources, and the retrieval layer. Tools first, prompts second.
Step 03
Eval and harden
Real eval set, traces, regression checks. We tune until the agent is boring — boring is good.
Step 04
Ship with humans in the loop
Approval gates on consequential actions. Auto-execution earned over time, per category, on evidence.

A good fit if

Teams with a clear, narrow job that an agent could do end-to-end
Companies that take observability and safety seriously
Operators ready to invest in evals, not just prompt tweaks

Not for

Buyers who want a fully autonomous agent with no human checks
Use cases where the cost of a wrong action is unbounded
Anyone hoping to skip evals and ship on vibes

Related demo builds

Systems we build with this service

Build-ready demos we adapt to client tools and data. They show what shipping a system in this category looks like.

Looking for the full library? Browse all Ideaplexa Products.

FAQ

Common questions about AI agent development

What makes an AI agent production-ready?: Grounding on real data, narrow scope, tool calls with auth and retries, traces and evals on every run, and explicit guardrails — rate limits, allowlists, approval gates, kill switches. If any of those are missing, the agent is a demo.
Do you build with the AI SDK, LangChain, or something else?: We default to the Vercel AI SDK and provider-native tool calling because they are the cleanest way to ship in production today. We will use other frameworks where a project genuinely needs them, not because they are trendy.
Can the agent run fully autonomously?: Eventually, in narrow categories, once evals show it earns the trust. Day one, every consequential action sits behind a human approval. We are not in the business of letting an open-loop agent loose on a customer's systems.

Build an AI agent

Tell us about the workflow, the product, or the problem. We will tell you, on a short call, whether Ideaplexa is the right team to ship it — and if not, who is.