Use Cases

Every Interactor use case, in one place.

Seven agent use cases, built on Interactor AgentOS for real teams. For each one: the problem it solves, what the agents actually do, and why it runs on AgentOS and ships with Interactor Build.

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Marketing

Media spend and client reporting, run by agents.

Ad Campaign Manager

Problem: Campaign setup is manual work repeated across every ad platform and every country team. Performance arrives as a weekly agency report, days after the money moved, and nobody is watching overnight.
Solution: Five specialist agents set channel mix, budget allocation, and KPI targets from your target ROAS, then generate campaign structures, audiences, tracking tags, and creative across four ad platforms — dry-running every API call before a dollar moves. They watch results daily and propose reallocations you approve in a click, reverted automatically if they underperform.
Why Interactor AgentOS + Build: AgentOS models the campaign world as software, so agents can only take allowed actions and scenarios are simulated before they touch a live budget — with budget caps, dual approval on large moves, a full audit trail, and automatic rollback. Live today in one market under a multi-year program rolling out one country at a time toward roughly forty.

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Senior Marketer

Problem: An agency's edge lives in the heads of a few senior marketers, and it keeps walking out the door. Their mornings go to the same loop on every account — read performance, forecast the month, set directions, assign tasks, write the client report — so coordination outweighs actual marketing.
Solution: Agents run the morning loop before the team logs on: monthly goals and yesterday's numbers become a month-end forecast and today's directions, each broken into tasks with an owner, a due date, and a priority. Every client's daily report is drafted by 9am, grounded in the agency's own playbook. A human reviews and sends.
Why Interactor AgentOS + Build: AgentOS orchestrates the specialist agents, retrieves the agency's codified know-how so every recommendation is cited rather than guessed, and connects warehouses, spreadsheets, and chat as tools — so numbers come from deterministic queries, never a model's memory. Approval gates, per-account data scoping, and full traceability come with it. In daily use by the agency's senior commerce team within weeks of kickoff.

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Finance

Underwriting decisions in minutes, not days.

Loan Underwriter

Problem: Applicants apply to three or four lenders at once, and the first solid approval usually takes the deal — yet underwriting still takes hours or days. The data lives in a dozen places, court searches drown in same-name false positives, and the one senior underwriter who can read it all is the busiest person in the firm.
Solution: Five agents work one file in parallel: months of bank statements read down to true monthly revenue and current leverage, a nationwide court and registry sweep cleaned of same-name noise, a red-flag panel with a confidence on every item, and the bottom line — existing obligations, maximum safe offer, daily payment. The firm's own credit policy runs deterministically on every deal.
Why Interactor AgentOS + Build: On AgentOS, statement analysis, court records, risk databases, and registry lookups connect as agent tools; the firm's underwriting rules live in a knowledge base agents must obey; and math and rules run as software while language models only read, extract, and summarize — same deal in, same decision out, every decision logged with its evidence trail. Decisions that took a day of a senior underwriter's time come back in minutes.

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Customer

Every inquiry answered. Every lead remembered.

CS / Sales Agent

Problem: Phone consultants work from printed Q&A sheets across five to seven disconnected tools. The inquiry that arrives at midnight waits until morning, follow-up depends entirely on a staff member's memory, and the consultant who knows every procedure and price can only take one call at a time.
Solution: A 24/7 consultation agent answers from the clinic's own knowledge — procedures, pricing, practitioners — reads the customer's situation, books the visit, and takes a deposit in chat. Human phone agents get live transcription, instant answer lookup, and auto-filled checklists. Agents pick the right moment to re-engage every lead, and a human approves every outbound message.
Why Interactor AgentOS + Build: AgentOS gives each clinic its own knowledge base, structured customer memory that survives months between visits, and booking, records, payment, and calendar systems connected as tools. A knowledge graph of procedures and rules controls what the agent may say, so contraindications are gated and medical claims are off-limits — structural guardrails the model cannot talk its way around, with full audit logging behind them.

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Product

The product manager who ships every day.

Product Manager

Problem: The product manager writes the spec, files the tickets, and waits for the sprint. Something gets lost in translation, engineering time is scarce, and by the time the feature ships the customer who asked for it has moved on.
Solution: The product manager states what customers need in plain language — no tickets, no specs, no code. Agents investigate the current codebase and everything already planned, then a fleet writes the feature, builds a full test suite, and reviews the work before any human sees it. Docs, help pages, and tutorials touched by the change ship in the same release, and every release requires an engineer's approval.
Why Interactor AgentOS + Build: This is the use case behind Interactor Build itself. AgentOS coordinates the agent fleet in software — retries, restarts, and the right model on each phase — keeps the product's state, plans, and conventions in a shared knowledge base, and exposes repos, CI, test runners, and docs as tools agents call. We build Build with Build: from request to released feature in ~4 hours, median, on our own product.

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Manufacturing

Every signal handled right at the source.

Edge Data-hub

Problem: Most edge platforms ship as a closed box: adding a protocol or changing a pre-processing rule means a change request to whoever built the platform, then a wait. Meanwhile every device streams raw data to the cloud whether anything downstream needs it or not, so upload costs climb, response time slips, and a cloud outage can stall the line.
Solution: Agents bring a new device online without a firmware change or a vendor ticket, then time-sync, filter, transform, and tag the signal at the edge before it ever leaves the device. Clean data is reshaped and routed to whatever is downstream — an ML pipeline, a historian, a dashboard. One console watches and updates every edge device across every line, and because the processing happens on-site, an outage doesn't stop the line.
Why Interactor AgentOS + Build: On AgentOS, protocols, pre-processing steps, and downstream integrations connect as agent tools added without touching the platform's core. It runs across operating systems and hardware architectures with a small footprint, pre-processing rules run as software rather than a language-model guess, and every configuration change and remote update is logged. Every new device, rule, or integration is one your own team can add.

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Telecom

One live picture of the whole network.

Observability

Problem: A typical NetOps stack runs a different monitoring tool per layer, each with its own dashboard and its own alerts. When something breaks, a human has to notice, open every tool, and stitch the story together before anyone can act — and most observability tools don't cover the network infrastructure at all.
Solution: Agents pull metrics, traces, and logs from software, hardware, and the network layer into a single stream, connect the events across layers to surface what is actually causing an incident, and rank it by blast radius and business impact. Pre-built, no-code workflows then carry out the routine investigation and remediation steps without waiting on a human to start each one.
Why Interactor AgentOS + Build: AgentOS plugs routers, switches, firewalls, and existing monitoring tools in as agent tools, so new data sources join the picture without custom integration work. Incident-response playbooks are captured once and run consistently instead of re-improvised by whoever is on call, and every automated action is logged with the evidence that triggered it. Root cause surfaces in minutes instead of a war-room call.

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The platform underneath

The agents differ by industry. What they run on doesn't.

Every use case above is the same two products, pointed at a different job.

The world-model and execution layer every agent needs — knowledge, service registry, database, orchestration, identity, and billing, premade. It is what makes numbers queried instead of guessed, actions gated by human approval, and every decision auditable.

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Interactor Build

AI that continuously improves your product: state the goal in plain language, and agents plan, build, test, and review the feature — with nothing released without your engineer's approval. Every use case on this page is built with it.

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Tell us the job you need done, and we'll show you the agents that already do it.

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