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AI agent development

AI agents built for narrow, useful jobs.

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 need a tight job and real guardrails.

In production, agents face messy data, broken APIs, unclear instructions, and adversarial users. The work is not just prompting. It is grounding, observability, permissions, retries, and clear rules for when a human needs to approve the next step.

  • 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

  1. Step 01

    Define the job

    What the agent does, what it does not do, what 'good' looks like, and what failure modes are unacceptable.

  2. Step 02

    Build tools and grounding

    We wire up the tools, the data sources, and the retrieval layer. Tools first, prompts second.

  3. Step 03

    Eval and harden

    Real eval set, traces, regression checks. We tune until the agent is boring — boring is good.

  4. 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
  • Projects that need automation without evaluation or human review

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, and kill switches.
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.