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Services·AI Flagship·Operate
Flagship engagement

Agent Operations Retainer

Your agent gets better every month — or we tell you why it isn't.

Agents drift. Models update. Tools change. Your business evolves. This retainer keeps your deployed agent sharp: weekly eval review, drift monitoring, prompt tuning, tool additions, monthly cost optimization, and a written report so you know exactly what we did and what changed. Cancel any month.

from $600/mo
Monthly · cancel anytimeMonthly subscriptionCustom pricing on request
Eval reviewDrift monitoringPrompt tuningCost optimizationTool additionsMonthly report

Compare flagship offers

Five engagements. Pick what matches your situation.

See all services
EngagementPriceTimelineModeBest for
AI Implementation Consulting
from $1,0002 weeksAuditDon’t know where AI fitsView
AI Agent Development
from $2,6004 weeksBuildRepetitive ops work eating your weekView
AI Voice Agent
from $1,8003 weeksBuildMissed inbound callsView
AI Lead Engine
from $2,2004 weeksBuildTargeted outreach without spamView
Agent Operations RetainerYou’re here
from $600/moMonthlyOperateAlready shipped — keep it sharp
Why this exists

Your agent has an on-call team. That team is us.

Agents drift. We catch it before your customers do.

AI agents fail in slow, quiet ways: a vendor changes a tool API, a model gets updated, eval scores creep down, costs creep up, edge cases stack up. Without monitoring you find out from a customer. We watch eval pass rates, spend trends, and the activity log every week. We tune prompts, add test cases, ship guardrails, and write a monthly retro you actually want to read. Cancel any month — no annual lock-in.

BYOK
Pay LLM providers direct
Eval harness
Regressions caught in CI
Spend cap
You set the ceiling
Human-in-loop
Approval queue, not auto-send
How it works

The architecture, end to end

No black boxes. Here\u2019s the actual shape of the system you get \u2014 with the guardrails, eval loops, and human approvals where they belong.

What we monitor every week

A real ops loop, not a dashboard you forget about.

feeds backEval pass ratetrend · regressionsSpend trend$ / task driftError logtool failuresWeekly reviewus, with youPrompt tuneprove with evalsNew test casesedge cases capturedMonthly retrowhat changed · why
inputcoreoutput
Where this fits

Real use cases we ship

You shipped the agent, now what?

Production AI is a moving target. Models update, APIs change, edge cases stack up.

No internal AI team yet

Buy ops capacity by the month instead of hiring a $180k role for a part-time job.

Multi-agent stack

Voice + ops + lead engine all running? Coordinated tuning so they don’t fight each other.

Compliance-sensitive industries

Documented changes, eval evidence, and audit-ready retros every month.

Your command center

The dashboard you actually use

Every flagship engagement ships with a stylized control panel \u2014 live activity, eval pass rate, spend cap, and an approval queue you can act on from your phone.

Live · Production

Agent Ops — This week

Sample retainer report. You get this every Monday.

Eval pass rate
96%
+1pp vs last wk
Spend / task
$0.041
−8%
Tool failures
4
3 fixed
New evals added
+7
edge cases
Live activity
last 5 min
  • MonTuned quote-drafting prompt: cut hallucinated SKUs from 3% to 0% in evals
  • TueAdded 4 test cases from this week’s approval-queue rejections
  • WedAnthropic API change — updated client lib, no agent downtime
  • ThuSpend cap raised $500 → $750 (you approved) — reflects 30% volume growth
  • FriSent monthly retro: 3 wins, 1 close call, 2 changes for next month
Eval pass rate96%
42 / 44 test cases passed · last run 12m ago
Monthly spend$612 / $750
Auto-pause at cap. Slack alert at 80%.
Awaiting approval1
  • Refund request > $250 — review
  • Outbound email batch — 12 ready
  • 1 more queued
Outcomes

What you get

Agent quality stays high — or you find out exactly why it dropped
New tools and workflows added as your business grows
Monthly cost optimization (model swaps, prompt compression, caching)
Drift detection so you catch problems before customers do
Written monthly report — what we did, what changed, what's next
Agent flow

How the agent thinks

The decision graph behind the engagement. Inputs, branches, and the point where a human stays in the loop.

Agent Operations Retainer CycleA cyclical operations loop: monitoring (latency, eval, cost) → drift detection → eval harness → patch deploy → reporting, with arrows returning to monitoring.1 · Monitoringlatency · eval · cost2 · Drift Detectiondistribution shift · regressions3 · Eval Harnessgolden set · A/B4 · Patch Deploycanary + rollback5 · Reportingweekly · monthlysignalrerunship fixrecordrollupCONTINUOUSmonthly retainerCYCLE · arrows clockwise · feedback never stops
Methodology

How we run this engagement

Concrete phases, concrete artifacts. You always know where we are and what comes next.

01
Week 1 of month

Eval review + drift check

Sample 20–50 real agent runs. Score against eval criteria. Flag anything that drifted. Review cost trends.

Eval scorecardDrift reportCost trend chart
02
Week 2

Tuning + improvements

Apply prompt + tool fixes based on eval findings. Add new tools or workflow expansions. Re-run evals to measure improvement.

Updated promptsNew tool integrationsBefore/after eval delta
03
Week 3

Cost optimization

Review model choice + prompt length + caching opportunities. Test cheaper alternatives where quality holds. Document savings.

Model comparisonCost savings reportUpdated configs
04
Week 4

Report + planning

Monthly performance report. Loom walkthrough of changes. Plan next month's priorities with you.

Monthly PDF reportLoom walkthroughNext-month plan
By the numbers

Typical results

on real runs
Weekly
Eval review cadence
or workflow expansions
2 new
Tools / mo included
no contracts
Cancel any month
Commitment
Deliverables

What ships

  • Weekly eval review on a sampled set of real agent runs
  • Drift monitoring with alerts when quality scores drop
  • Prompt + tool tuning based on eval findings
  • Up to 2 new tool integrations or workflow expansions per month
  • Monthly cost optimization review (model choice, prompt length, caching opportunities)
  • Monthly performance report (PDF + Loom)
  • Slack channel with 1–2 business day response on issues
  • Quarterly strategy call to review trajectory
Not included

Out of scope

  • Brand new agent builds (use AI Agent Development)
  • Major architecture rebuilds (separate engagement)
  • On-call / 24/7 support (use Reliability Retainer for that)
Add-ons

Extend the engagement

Additional agent

+$360/mo

Add a second agent under the same retainer scope.

On-call support

+$600/mo

24/7 pager for agent-down incidents with 1-hour response.

Sample deliverables

See the artifact, not the marketing.

Real shape, redacted content. Pick a tab to preview what ships.

Sample Audit Report

Twelve-page audit excerpt: scope, methodology, findings ranked by impact, and a prioritized fix list. Redacted.

Sample provided after intro call · ask sage@sageideas.dev

SAMPLE · REDACTED
How we reduce risk

Money-back if you're not happy in week 1

Reset the engagement before momentum builds. No invoices to dispute, no awkward email.

Async-first, weekly demos, no surprises

You see exactly what shipped each week. No status meetings to attend, no reports to chase.

Code is yours from day 1 — no lock-in

Your repo, your infra, your accounts. We work in your stack. You can take the work in-house at any time.

FAQ

Honest answers

Why do agents need ongoing operations?

Three reasons: (1) Models change — what worked on GPT-4-0314 may not work on GPT-5. (2) Your business changes — new tools, new processes, new edge cases. (3) Drift is real — without monitoring, quality degrades silently. The retainer makes this someone's job.

What if I built my agent with someone else?

We can take over operations on agents we didn't build, but we need a 1-week onboarding to map the architecture and stand up our eval harness if you don't already have one. Onboarding is included in the first month at no extra cost.

Can I cancel?

Any month, no commitment. We give you the playbook and dashboard access on the way out so your team can take it over.

How does this compare to hiring an AI engineer?

An in-house AI engineer costs $8–15k/mo loaded. This is a fraction of that, with a tighter scope (agent ops only). If you have multiple agents and need broader engineering, hire an engineer. If you have one or two agents and need them maintained well, this is the play.

Ready to scope this for your business?

Book a 30-minute discovery call. No pitch deck. We'll either confirm fit and send a proposal, or tell you straight that this isn't the right move.

livebuild d7ed89b2026-06-08 06:36Z
// solo studio// no analytics resold// every commit human-reviewed