How Phronimos builds and runs your AI agents
Review first. Build second. Manage ongoing. A paid diagnosis finds where work leaks and scopes the build. A sprint puts the first operator into production. A monthly retainer keeps it running and adds the next one. Each step ends in something you can hold, and each one is a decision point.
Start here: what an AI workflow audit includes, or readhow we handle access to your systems.
Agent Reliability Review
Find the operational leak.
A focused, paid diagnosis of where work is leaking and which workflows should be handled first. Bounded, useful, and concrete.
What happens
- 45-minute recorded discovery call
- Workflow and tool-stack review
- Bottleneck analysis
- Custom findings report with 3 to 5 recommendations
- ROI baseline estimate
- 30-minute review call
- Recommended first-sprint scope
The outcome
A clear diagnosis of where time is leaking and which one or two workflows should move first. You will know what should stay human, what should become a workflow, and what should become a managed AI operator.
Agent Implementation Sprint
Build the first operator.
One workflow gets designed, built, tested against live examples, and handed over with guardrails and documentation.
What happens
- First-operator design
- Agent and runtime configuration
- Authentication and integration setup
- Testing with live examples
- Guardrails and escalation paths
- SOP and handoff notes
- Team training session
The outcome
One workflow becomes measurably faster and less owner-dependent. The deliverable is a running system with a documented maintenance path, and a baseline to measure it against.
Fractional AI Officer retainer
Keep the system running.
The retainer makes AI adoption a line in your operating budget with an owner attached. This is the ongoing partnership.
What happens
- One to two active requests at a time
- Under-48-hour turnaround target
- Monitoring and failure alerts
- Maintenance and tuning
- New workflow rollout
- A customer-facing request queue
- Async video updates on work delivered
- Continuous optimization
The outcome
An operator layer that keeps expanding without your attention, run by a partner who is accountable for it working.
What happens when an AI agent breaks?
Every operator carries monitoring, failure alerts, automatic recovery where it is possible, and a named escalation path. Alerts reach Phronimos before they reach you, and every incident ends in a written account of what happened and what changed.
An agent that works most of the time is a liability, because the failures are silent and the cost lands on whoever was trusting it. So reliability is engineered first and measured continuously, on our side of the line.
Start where the risk is lowest.
The review is a bounded first step with a concrete deliverable, and it is the only commitment you make up front. Everything after it is a separate decision.