Skip to content
Book a call
Book a call

Service

Autonomous AI Agent Development

Work that a flowchart cannot describe?

VelvoAI builds agents for the jobs where the next step depends on what the last one found. The agent decides the sequence, calls the tools it needs, and stops to ask a person when it should. This is the layer above automation: judgment, not just ordering.

Book a callSee selected work

Built by VelvoAI, an independent AI systems studio. One engineer scopes and writes every system.

What the system does

Step reasoning
Multi
Human approval
Gated
Every action
Traced
Autonomous AI Agents · typical shape
SystemAutonomous AI Agents
  1. ComponentLangGraph
  2. ComponentOpenAI
  3. ComponentAnthropic
  4. ComponentFastAPI
  5. ComponentPostgres

The problem

Why this keeps costing you

Some work resists rule-based automation because the path changes every time. Researching a prospect, triaging a support ticket that spans three systems, reconciling a discrepancy across accounts. The steps are knowable but the order depends on findings, so a static workflow either misses cases or grows into an unmaintainable tangle of conditions.

What we build

Inside an autonomous AI agent we ship

  • Planner-executor architectures that decompose a goal into verifiable steps
  • Tool-use layers with typed inputs and strict permission boundaries per tool
  • Human-in-the-loop approval gates on anything irreversible or high-value
  • Memory and state management so an agent can resume long-running work
  • Multi-agent handoffs where a specialist agent takes over a subtask
  • Full execution traces showing every decision, tool call, and intermediate result

How we approach it

The part most implementations skip

We scope agents narrowly and give them explicit permissions. An agent that can read from five systems and write to one is dramatically safer than a general-purpose assistant with broad access. Every action is traced, every decision is inspectable after the fact, and anything irreversible waits for human sign-off.

Evidence

Where this has been built

Each project states its status and the role behind it.

What the status tags mean
A terminal session of the coding agent: it reads a file, proposes a write, and asks for approval before writing
A real session in the terminal

01 / 01Own build

Coding Agent

A LangGraph Coding Agent

Self-built · personal project, not client work

Problem
A coding assistant that can write files can also overwrite the wrong one, so the real problem is giving a model file access without giving it the final say.
What was built
Every write shown first and applied only on approval, with a backup kept
Stack
LangGraphOpenAIPythonRichPydantic

Coding Agent · how a request is handled

  1. Take the request
  2. Route to a tool
  3. Run the file tool
  4. Ask before writing
  5. Stream the answer
Read the case study

Deliverables

What you actually receive

Every VelvoAI engagement ends with working software you own, not a slide deck. Here is what ships with an autonomous AI agent.

How a build runs, step by step
  1. D.01Agent scope document defining goals, tools, and hard permission limits
  2. D.02Evaluation suite with recorded scenarios and expected outcomes
  3. D.03Trace viewer for inspecting any run step by step
  4. D.04Approval interface for the gated actions in your workflow
  5. D.05Cost and latency instrumentation per agent run

By industry

Autonomous AI Agents in your sector

Autonomous AI Agents, applied to the workflows and rules of the sectors we work with most.

All industries

Common Questions

01How do you stop an agent doing something wrong?

Two mechanisms. First, permissions: an agent can only call the tools you granted it, and write actions are scoped as narrowly as the job allows. Second, approval gates on anything irreversible — sending external email, moving money, deleting records — so a person confirms before it happens.

02How is this different from workflow automation?

Automation follows a path you defined in advance. An agent chooses the path at runtime. If you can draw the process as a flowchart that does not need constant new branches, automation is cheaper, faster, and more predictable — we will tell you when that is the better fit.

03How do you know the agent is actually working?

An evaluation suite built from real cases, run on every change. Without it you have no way to tell whether a prompt tweak improved things or quietly broke a case you fixed last month.

Related services

Often built alongside this

Written by
Usman Ali, Founder
Last reviewed

Next step

Ready to scale operations without more busywork?

Bring the workflow, lead leak, reporting gap or knowledge bottleneck. You talk to the engineer who would build it, and leave knowing what it would take to ship.

Book a call

Free, no obligation.

Or start from a problem we fix