How we work
Not a project. A partnership.
We don't build and disappear. The engagement doesn't end when the agents are deployed.
An operations mapping session. We document how the business actually operates — what decisions live in the owner's head, what falls through when the usual person is out, what the team does every day that nobody has ever written down.
Output: A prioritized list of 5–8 automation candidates, ranked by: time cost, consistency risk, and revenue proximity.
A phased build sequence. We define exactly which agents get built, in what order, connected to which systems. Each agent spec includes: trigger, logic, output, measurement, and override path.
Output: A signed-off build plan. The owner approves every agent before any code is written.
Agents deployed on the client's infrastructure, connected to systems already in use. We don't build standalone apps — we build integrations that live inside the tools the business already uses (Teams, Slack, Google Workspace, existing CRMs).
Output: Production agents running on your infrastructure, with monitoring and override controls.
After deployment, we stay. Monitoring, reporting, optimization, and evolution as the business changes. The agent learns your business — your preferences, the edge cases, the way decisions actually get made — so day 90 looks nothing like day 1. Most engagements evolve: new agents get added, existing ones get refined, the system grows.
Output: Monthly reporting on agent performance. Ongoing optimization. Agents that compound — every month they understand more of your operation. New agents added as the roadmap expands.
What we watch for
The first things that break in production aren't the model.
After running agents 24/7 against real operations, the failures are predictable. The reasoning is rarely the issue. These five patterns are what we design around from day one — and what the audit looks for before any code is written.
The handoffs break before the model does
An agent qualifies the lead, but the CRM update silently fails. A voice assistant books the appointment, but the calendar field is the wrong format. The agent looks like it worked. The workflow didn't finish. Reasoning is rarely the failure point — transitions between systems are.
Source data gets messy fast
Agents are only as reliable as the operating environment around them. Old SOPs, duplicate customer records, half-updated docs, conflicting notes. None of that is the agent's fault — and all of it makes the agent look unreliable. In multi-agent systems, small data errors compound across steps.
Exception handling is the whole game
The demo path always works. Production is all edge cases. People reply out of order. A customer asks two things at once. An API times out. A staff member changes a record mid-flow. Without explicit rules for retries, fallbacks, and human review, trust leaks fast.
Ownership goes fuzzy at 2 a.m.
When a 24/7 workflow fails, whose job is it to notice? Ops? Sales? The founder? The most underrated source of production failures isn't a bug — it's nobody owning the outcome end to end. Agents need a named human who watches them.
Too much autonomy, too early
Owners want full automation on day one. Almost every workflow needs a staged rollout: first assistive, then partially automated, then higher autonomy once the error patterns are understood. Skip the staging and you get cleanup work, not leverage.
Security posture
Built for production. Not for demos.
Every agent is built with production security requirements from day one.
Credential handling
- →All API keys and credentials stored in Azure Key Vault or equivalent secrets manager — never in code
- →Secrets rotated on schedule, not just at breach
- →Principle of least privilege: each agent has only the permissions it needs
Data handling
- →No customer PII stored in agent logs
- →Outputs go to designated channels — never to personal inboxes or uncontrolled surfaces
- →Audit trail for every agent action
Human override
- →Every agent has an explicit override path
- →High-stakes actions (negative review responses, POs over threshold) require human approval before posting
- →Kill switch available for every agent — one setting change stops it
Scope control
- →Agents do exactly what they're built to do — nothing else
- →No internet access unless required by the agent's function
- →Output channels are whitelisted at configuration time
Tech stack
Standard tools. No lock-in.
Orchestration
- Microsoft Power Automate
- Azure Logic Apps
- n8n (self-hosted)
AI
- Claude (Anthropic)
- GPT-4o (OpenAI)
- Model selection per agent based on task requirements
Infrastructure
- Azure (primary)
- Vercel (web)
- GitHub Actions (deployment)
Integrations
- Microsoft 365 / Teams
- Google Workspace
- Most scheduling and CRM systems via API
Common questions
Things owners ask before they sign.
If yours isn't covered here, reply to hello@operably.ai and we'll get you a real answer the same day.
How much of my team’s time will this take?+
During discovery, about 60–90 minutes once. During build, ~30 minutes per week for review and feedback. After launch, near-zero unless you want to expand the system. The agents are designed to remove team time, not add it.
What if we don’t have clean data or documented processes?+
That’s normal — most owner-operated businesses we work with don’t. Step 01 (Discover) maps your operations as they actually run today, including what’s in someone’s head and what’s undocumented. The system we build accommodates the messy reality first, then we clean up where it makes sense.
What if the agent gets something wrong?+
Every agent ships with an explicit override path and a kill switch. High-stakes actions (negative review responses, payments over a threshold, anything client-facing) require human approval before posting until you decide otherwise. Your first agent ships in 4 weeks running production — if it isn’t working as defined, you don’t pay setup until it is.
Do we need an IT person or tech team?+
No. Agents are deployed on infrastructure we manage and integrate into tools your team already uses — Teams, Slack, Google Workspace, your CRM. The owner gets the override controls. The team interacts with the agent the way they’d interact with a colleague over email or chat.
What happens to our data and the agents if Operably goes away?+
Your data stays in your accounts — your CRM, your Google/Microsoft tenant, your storage. Agents run on your infrastructure, not ours. We provide an exit clause in every engagement: the agent code, prompts, and configuration are deliverable to you (or a future vendor) on request. No lock-in.
What’s locked in vs. swappable?+
Models are swappable — we pick the right one per task and can change it later without rebuilding the agent. Integrations are swappable — if you change CRM, the agent reconnects. The agent’s scope and the rules it operates inside are yours. Hosting + monitoring is what the monthly subscription covers; agents themselves are owned outright after setup.
Stop executing. Start governing.
The worst case: you do the mapping session and leave with a clearer picture of what's costing you — before spending anything on a build.
Start with an operations audit →