AI service
AI agents that act on your systems, within limits you set
Multi-step workflows that look things up, take actions through your APIs and stop for approval when the risk is high. Every step is logged and every permission is explicit.
An agent is more than a chatbot with a longer prompt. It plans a task, chooses tools, calls them, looks at the results and keeps going until the job is done or it needs help. That makes it powerful and, if built carelessly, dangerous. The same loop that books a meeting correctly can also email the wrong person or modify the wrong record when instructions are ambiguous.
We build agents with the controls that make them dependable. Each tool has a narrow purpose and a permission check. Actions are classified by risk: reading data may run freely, drafting happens in a queue and anything that spends money, deletes data or contacts a customer waits for a person. Every step is recorded, so you can replay what the agent saw, decided and did.
We start with a workflow that already exists, such as researching a lead, preparing a quote, triaging a support case or reconciling an order, and build the agent around its real steps. Our own platform, Delvix Revenue OS, uses this kind of pattern: an AI agent that answers replies, with audit logs and per-tenant keys. We apply the same discipline to your use case.
The problems
What usually goes wrong
Agents that do too much
Broad access and vague instructions let an agent take actions nobody intended. One misread request can touch real customers or data.
No way to see what happened
When an agent produces a bad outcome, there is no trace of its reasoning, the data it used or the tools it called, so fixing it is guesswork.
Brittle multi-step flows
A single failed API call or unexpected response derails the whole run. Without retries and fallbacks, agents work in demos and break in production.
Automation without accountability
If nobody owns the agent's actions, errors become nobody's problem. Teams then switch the agent off rather than improve it.
What you get
Deliverables
Agent specification
The goal, available tools, decision rules, escalation conditions and boundaries of the agent written in plain language.
Tool layer on your APIs
Narrow, well-described tools wrapping your systems, each with input validation and a role-based permission check.
Risk-based approval policy
Rules that decide which actions run automatically, which wait for approval and which are never allowed.
Run logs and replay
A record of every step, tool call and output, searchable by run, so problems can be diagnosed and fixed.
Evaluation set
A collection of realistic test scenarios you can rerun when prompts, tools or models change.
Operations and handover notes
Runbook for failures, limits and spending controls, and guidance for adjusting policies as trust in the agent grows.
How it works
Step by step
01
Choose a workflow
We pick a process with defined steps and a measurable outcome, and write down who does it today and where they get stuck.
02
Define tools and boundaries
We list the actions the agent needs, what each can touch and which require approval, before any model is involved.
03
Build with a human in the loop
The first version proposes actions and a person approves all of them, so we learn how the agent behaves on real cases.
04
Evaluate and loosen carefully
As logs show reliable behavior, we move low-risk actions to automatic and keep the risky ones behind approval.
05
Operate and improve
We hand over monitoring, limits and a process for reviewing failures so the agent gets better over time.
Timeline
How long it typically takes
A first agent for a well-defined workflow, running with human approval, commonly takes four to eight weeks from approved scope. Agents with many tools, several integrations or complex policies take longer, typically two to four months. Moving actions from approval to automatic is intentionally gradual and driven by evidence from the logs.
Who it's for
- Teams with a repeatable multi-step process that people currently do by hand
- Products that want agents to act on behalf of users under strict permissions
- Operators who need an audit trail for every automated action
Who it's not for
- Teams expecting a fully autonomous agent with no oversight from day one
- Processes that are not yet defined well enough for a person to explain
- Use cases where no safe, reversible actions exist and nobody can approve
Honest scoping
Where AI helps, and where it doesn't
Where AI helps
- Researching a lead or account and assembling a brief from several sources
- Triaging inbound requests and preparing the response for approval
- Reconciling records across systems and flagging mismatches for review
- Handling routine multi-step admin tasks that follow clear rules
Where it doesn't
- Open-ended judgment calls that need accountable human decisions
- High-stakes irreversible actions with no approval step
- Processes that change daily with no stable rules to encode
- Tasks where a simple scripted automation would be cheaper and more predictable
Data privacy
Your data stays yours
Agents run on your own provider keys, and the tools they call live in your environment under your permissions. Credentials are encrypted at rest when we must store them, and each agent only receives the data a given run needs. We do not use your data to train models through our application layer. Provider-level policies apply to your keys, and run logs stay in your database, where you decide retention and who can read them.
How we handle security →Keep exploring
Related pages
FAQ
Frequently asked questions
How is an agent different from a chatbot?
A chatbot answers. An agent plans and takes actions through tools, often over several steps. That is why agents need permissions, approvals and logs that a simple chatbot can live without.
What stops an agent from taking a harmful action?
Several layers: narrow tools, permission checks outside the prompt, input validation, risk-based approval and hard limits on spending and volume. We do not rely on the prompt alone.
Can we see why the agent did something?
Yes. Each run records the inputs, tool calls, results and outputs, so you can replay and review decisions, and use real cases to improve behavior.
Does it need to be fully autonomous to be useful?
No. Many valuable agents prepare work for a person to approve in seconds. Autonomy is something you grant gradually, for specific low-risk actions, once the logs justify it.
What happens when a provider is down or a tool fails?
Runs retry where safe, then stop in a clear state and notify the right person. We design for partial failure so a single error does not leave records half-changed.
Next step
Tell us what you want to build
A short call, then a written scope in plain language before any build starts.
- Fixed scope
- Weekly demos
- You own the code