How do we stop an agent damaging a customer account?

Narrow tool permissions, read-heavy write-light scoping, and approval gates on anything irreversible. Every action is traced, so when something does go wrong you can see precisely which step caused it rather than guessing.

How do we test an agent before customers see it?

An evaluation suite built from recorded real scenarios with expected outcomes, run in CI. Without one you cannot tell whether a prompt change improved behaviour or broke a case fixed last month.

How 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.

How 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.

Autonomous AI Agents · SaaS

Autonomous AI Agents for SaaS

Some work in your product needs judgment, not a wizard.

Multi-step work inside a SaaS product — configuration, migration, investigation, remediation — resists step-by-step wizards because the path depends on what the previous step found. Agents handle that branching with scoped permissions and approval gates on anything a customer would not want done silently.

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Step reasoning: MultiStep reasoning
Human approval: GatedHuman approval
Every action: TracedEvery action

Typical stack

LangGraph
OpenAI
Anthropic
FastAPI
Postgres

Use cases

What this handles for SaaS companies

Automate account setup and data migration that varies by customer environment
Investigate and diagnose issues across logs, config, and usage before escalating
Run guided remediation with customer approval on anything destructive
Handle complex configuration requests that would otherwise become support tickets

Work that previously required a support engineer completed in-product, with an inspectable trace.

Context

The saas problem underneath it

SaaS teams rarely lack AI ideas. They lack the specialist capacity to build them properly alongside an existing roadmap — retrieval that is actually accurate, agents that are safe to let loose in a customer account, and inference costs that do not quietly destroy gross margin.

The AI feature on the roadmap keeps slipping behind committed work
A prototype exists but nobody is confident enough to put it in front of customers
Inference cost per account is unmodelled, so pricing is guesswork
Support volume grows with the customer base and headcount follows

What we build

Inside an autonomous AI agent

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.

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

Deliverables

What ships with the engagement

Agent scope document defining goals, tools, and hard permission limits
Evaluation suite with recorded scenarios and expected outcomes
Trace viewer for inspecting any run step by step
Approval interface for the gated actions in your workflow
Cost and latency instrumentation per agent run

FAQ