Work is scattered
Work lives across email, Slack, spreadsheets, and tribal knowledge. It never quite lands in one accountable system.
Rettare Agent Ops
Operational AI, not experiments.
AI automation that behaves in production - with an Agent Ops layer.
Rettare Agent Ops is how we implement AI-assisted workflows so they hold up under real exception pressure: guardrails, logging, fallbacks, approvals where needed, and clear ownership. Not just prompts.
Send one messy process. We will tell you what is worth automating, what to leave alone, and what it takes to ship safely.
Why it matters
If your ops work feels expensive, it is usually because it is unreliable.
Most teams are not drowning because the work is hard. They are drowning because it is inconsistent.
Exceptions eat the day
Backlogs grow, handoffs break, and the strange edge cases become the operating model instead of the exception.
Trust erodes fast
SLAs slip, customers and stakeholders get impatient, and rework becomes the normal tax on every team.
Tools get trialled, then abandoned
AI pilots do not usually fail on capability. They fail because production risk is real and no one wants to own the mess.
AI pilots do not fail on capability. They fail on operations.
What Agent Ops means
Agent Ops is the operating layer that turns AI capability into controlled execution.
Not one magic bot. A system that coordinates narrow agents and humans with explicit rules for what happens, what gets checked, and who is accountable.
Intake and routing
What comes in, how it is classified, and where it goes next without losing ownership.
Context assembly
What the agent is allowed to see, why it can see it, and how that context is constrained.
Structured outputs and validation
What must be true before anything happens downstream and how the workflow checks that.
Approval gates
Where humans stay in the loop for risky, customer-impacting, or irreversible actions.
Run logging, monitoring, and rollback
What happened, when it happened, what tools were called, how exceptions were handled, and how the workflow gets improved without becoming another unmanaged experiment.
What we deliver
You do not just get prompts. You get artefacts your team can run.
Workflow spec + exception library
The happy path plus what happens when reality disagrees.
SOPs + Agent Runbook
How your team runs the workflow day to day, including escalation and ownership.
Governance policy
Permissions, approvals, audit, PII handling, and retention rules matched to the workflow.
QA + eval test set
So future changes do not silently degrade quality or create new failure modes.
KPI scorecard + exec reporting
Baseline versus current: cycle time, cost per case, SLA impact, error or rework, and exception volume.
Risk controls
We build safe by default, then earn autonomy.
Least privilege
Service accounts and scoped access per workflow.
Draft-first by default
Shadow to Draft to Execute, rather than straight to autonomous action.
Human approval gates
Irreversible or customer-impacting actions stay gated until the workflow earns trust.
Structured outputs + validators
Checks happen before any side effects fire downstream.
Kill switch + rollback
Tested recovery paths, not implied ones.
Full run logging
Inputs, tools called, outputs, approvals, and run IDs are traceable.
PII redaction + retention
Sensitive data handling is designed up front, not bolted on later.
SLOs + alerts
Quality, latency, cost per run, and failure rate are monitored as operating metrics.
If a workflow cannot be governed, we do not ship it.
Delivery posture
We optimise for production behaviour, not demo behaviour.
Step 01
Start with a workflow spec + exception library
We map the intended flow, the ugly edge cases, and the operating decisions that matter later.
Step 02
Implement draft-first
Shadow first, then Draft, then Execute once the workflow has earned the right to act.
Step 03
Add approvals for high-impact actions
High-risk actions stay gated until the workflow demonstrates repeatable quality under real volume.
Step 04
Log runs with audit trails
Inputs, outputs, timestamps, and approvals stay visible so the workflow is explainable when something goes wrong.
Step 05
Build fallbacks for tool and API failure
We assume dependencies will fail and design clear manual paths before that happens.
Step 06
Monitor exceptions and ship controlled changes
The workflow keeps improving without becoming an unowned experiment. If you want the week-by-week plan, we will send it after the fit check.
How to engage
We keep the offers menu consistent. Agent Ops sits inside each engagement.
This is not a second pricing ladder. It is the operating standard we apply inside the AI Automation offers menu.
Discovery Sprint
Clarity + plan
Turn “we should automate this” into a buildable backlog with sequencing, acceptance criteria, and governance from day one.
Workflow Build
One workflow shipped
Ship one workflow end to end with integration, testing, documentation, ownership, and basic monitoring.
Rollout Program
Automate across a team
Automate across a team with shared components, dashboards, exception handling, and change management.
Managed Ops
Keep it healthy
Keep automations healthy with monitoring, incident response, iteration, vendor and API change handling, and reporting.
If you are unsure which entry point fits, send one messy workflow and we will recommend the smallest engagement that can deliver a real outcome.
See the implementation scopeFAQ
Common objections, handled directly.
“We tried AI and it did not work.”
You probably trialled a tool. We deliver a governed workflow with runbooks, QA and evals, monitoring, and a named owner cadence so it stays reliable.
“Our work is too custom.”
Most workflows have a repeatable 70 to 90 percent layer. We codify exceptions and route judgment to humans by design.
“What about security and compliance?”
Least privilege, approval gates, audit logs, PII handling, retention, and rollback are not add-ons. They are the default.
“Who owns this internally?”
We require a named Internal Agent Owner. If there is no owner, the system will decay, so we do not start without one.
Final CTA
Book a 20-minute fit check
Send one messy process. We will tell you what is worth automating, what to leave alone, and what it takes to ship safely.