AI Agents & Automation
Managed AI agents that keep getting better after launch.
For teams with an agent in production and nobody whose job is to watch it. We monitor every run, review real conversations, test each change before it ships, track cost and speed, and respond when something breaks. It works for agents we built and for ones you already run.
- A person replies within one business day
- A plan you can act on
- You own every asset
- Onboarding
- 2 to 4 weeks to full monitoring
- Engagement
- Monthly retainer
- Coverage
- Agents we built or ones you run
- Reporting
- Monthly quality, cost and incident report
Reviewed by Noor Khan
Agent monitor· Traces · tests · report
SampleEvery run of a live support agent is traced, an order lookup fails and raises an alert, a prompt fix passes all 48 test cases before it ships, and the monthly report shows the pass rate rising and cost on budget.
- Every run is traced
- Alert flags a failure
- Fix passes the tests
- Monthly report lands
Illustration with sample data. Your version uses your accounts and numbers.
Brands Noor has worked with
Overview
What are managed AI agents?
Managed AI agents are production AI agents that a dedicated team monitors, evaluates and improves on an ongoing basis after launch. The work covers tracing every run, scoring answers and actions against test cases, reviewing real conversations, updating prompts and tools, upgrading models safely and tracking cost and latency. It's the operations layer, often called AgentOps, that keeps an agent accurate as your business, your data and the models underneath it change. Noorscale runs this for agents we built and for agents your team or another vendor built, with a monthly report on quality, cost and incidents.
See it in action
What does a managed AI agent engagement look like when it's working?
It looks like the live sample below, built with illustrative data. Your version runs on your own accounts and your numbers.
AI chat agent · website widgetNorthwind Accounting · pricing page
Chat ready
CRM record · created by the agent
Filling in- Source
- Industry
- Locations
- Transactions
- Owner
Routing rules
Waiting- Good fit: offer a call with a specialist
- Later stage: send a resource, add to nurture
- Discount, complaint or 'a person, please': a teammate joins
Illustrative chat. Your agent answers only from your approved content, books into your team's calendars and brings in a person by the rules you set.
Sound familiar?
What problems does a managed AI agent engagement solve?
It solves the three problems teams raise most often on a first call, and each one has a fix.
The problem
- “Our agent was great at launch and has drifted since”Products, prices and policies changed, the model was updated, and nobody re-tested, so answers slowly got worse.
- “We hear about failures from annoyed customers”Without tracing and alerts, a broken tool call or a wrong answer can run for days before anyone on the team notices.
- “Nobody owns the agent now the project is over”The builders moved on, prompts live in someone's notes, and every change feels risky because nothing is tested before it ships.
What changes
- Problems caught before customers noticeAlerts and regular reviews surface failures early, instead of after a complaint or a bad review.
- An agent that improves every monthReal conversations feed new answers and test cases, so the same mistake doesn't happen twice.
- Safe changes and model upgradesEvery update is tested and released gradually, so improving the agent stops feeling risky.
- Clear numbers on quality and costOne monthly report shows accuracy, spend, speed and incidents, so you know what the agent is worth.
Compare your options
Your own team, a standard support contract or managed agents?
Each option suits a different stage. Here's how they compare on the points buyers ask about most, including where the others do well.
| Compared on | Your own team | Standard software support | Managed by Noorscale |
|---|---|---|---|
| Knows the agent's history | Your own teamBest, if the builders stayed | Standard software supportOnly what's in the tickets | Managed by NoorscaleOnboarding audit, then full context |
| Catches quiet failures | Your own teamIf someone reads conversations | Standard software supportFixes bugs you report | Managed by NoorscaleConversation review and scored tests |
| Model upgrades | Your own teamYour team tests and decides | Standard software supportUsually outside the contract | Managed by NoorscaleTested, released gradually, easy rollback |
| Cost model | Your own teamStaff time you already pay | Standard software supportFixed support fee | Managed by NoorscaleMonthly retainer by agents and volume |
| Ongoing effort for your team | Your own teamFull ownership and on-call | Standard software supportReport bugs and check fixes | Managed by NoorscaleOne owner reviews a monthly report |
| Best when | Your own teamYou have AI engineers with time | Standard software supportBugs are the main risk | Managed by NoorscaleYour agent is live and nobody owns it |
On the free call, Noor will tell you which option suits your stage, even when it's one of the others.
What's included
What does a managed AI agent engagement include?
A managed AI agent engagement includes six deliverables, each one working material your team can use, not a slide deck.
Scope · Managed AI Agents6 deliverables
- 01
Tracing and live monitoring
Every run traced from input to answer or action, with dashboards and alerts for errors, failed tool calls, slow responses and unusual spend.
- 02
Evaluation suite and release checks
Real cases scored for correct answers, correct actions, refusals and handoffs, run before every prompt, tool or model change.
- 03
Conversation review
A regular sample of real conversations read by a person and tagged by outcome, with failures turned into fixes and new test cases.
- 04
Prompt, tool and model updates
Changes kept in version control, tested against the suite, released gradually and easy to roll back, including moves to newer models when they test better.
- 05
Cost and latency tracking
Spend and response time per conversation or task, with budgets, alerts and savings such as caching or smaller models where they pass the tests.
- 06
Incident handling and monthly report
A runbook to pause, roll back or hand off when something breaks, a written review after each incident and a monthly report on quality, cost and changes.
Tools and platforms we work in
Langfuse- LLangSmith
- AArize Phoenix
- HHelicone
- OOpenTelemetry
- PPromptfoo
- DDatadog
- GGrafana
Sentry
GitHub
Claude
OpenAI GPT models
Process
How long does a managed AI agent engagement take?
The work runs in four steps, starting in weeks 1 to 2 and then running as ongoing work. Every step ends with something you can use, so you always know what's landed and what comes next.
Step 01Weeks 1 to 2
Audit the agent as it runs today
We review the code, prompts, tools, data access and recent conversations, then list the risks and the quick fixes.
You get: Written risk list and quick fixes
Step 02Weeks 1 to 3
Set up tracing, alerts and baselines
We add tracing and dashboards, agree alert thresholds with you and measure today's accuracy, cost and speed as the baseline.
You get: Dashboards, alerts and a measured baseline
Step 03Weeks 2 to 4
Build the evaluation suite
We turn real conversations and known failures into scored test cases, so every future change gets checked first.
You get: Scored test suite from real conversations
Step 04Ongoing
Run, review and improve
We watch alerts, review conversations, ship tested fixes and send a monthly report on what changed and what it did.
You get: Tested fixes and a monthly report
Reporting
How will you know a managed AI agent engagement is working?
You'll see it in four numbers on a live dashboard, with a written review every month. Leading indicators come first, then the numbers your finance team cares about.
Monthly reportSample
- Evaluation pass rateHigher is better
Score on the test suite before each release, tracked over time so regressions are caught before users see them.
- Task success rateHigher is better
Share of conversations or tasks the agent completes correctly without a person stepping in.
- Cost per conversation or taskLower is better
Model and hosting spend per unit of work, against the budget you've set.
- Time to detect and resolveLower is better
How quickly an incident is spotted, and how quickly it's contained and fixed.
15 minutes with Noor
Noor Khan, Founder & CEO
You'll leave with
- Your biggest growth opportunity, in plain terms
- The first two or three moves worth making
- A clear plan for your first milestone
Noor listens first, then shows you the fastest route to the result you want.
Useful to have with you
- Your website address
- The goal or problem you want to solve
- Any numbers you already track
Booking opens on Cal.com. Or send a call request
Who it's for
Who is a managed AI agent engagement for?
A managed AI agent engagement works best for teams in one or more of these situations.
- Companies with a chat, voice or back-office agent already in production
- Teams whose agent builders have moved on to other projects
- Agents that talk to customers, where every mistake is visible
- Businesses facing a model upgrade they don't want to risk alone
Engagement
How is a managed AI agent engagement scoped and priced?
Pricing is shared after the free call, in a written scope with deliverables, timeline and fee. Never a package, never a surprise.
- Typical for this service
Monthly retainer
Ongoing work against a 90-day plan, for channels that compound month over month.
Usually: Short onboarding audit, then a monthly retainer sized by the number of agents and their volume
- Monthly report and strategy call
- Priorities reset every quarter
- No long lock-in
Fixed-scope project
A defined scope, timeline and fee for builds, audits, migrations and launches.
- Deliverables agreed in writing
- Milestones you can check
- Optional care plan after launch
Focused sprint
A short, time-boxed piece of work on one priority, to prove the approach first.
- One clear goal
- Working output, not a deck
- Roll into a retainer or stop
Every engagement includes
- Onboarding audit and baseline
- Tracing, dashboards and alerts
- Evaluation suite run before every change
- Conversation review, updates and model upgrades
- Incident handling and a monthly report
What shapes the scope
- Number of agents we look after
- Traffic each agent handles
- Systems the agents touch
- How fast incidents need a response
- State of the existing code, prompts and tests
You'll see how each one applies to you in the written scope.
Who leads the work?
Founder-led by Noor Khan
A software engineer turned AI and growth strategist. Noor sets the strategy and architecture on every engagement, reviews every plan before it ships and built the method the whole team works by.
FAQ
Questions about Managed AI Agents
Something else on your mind? Email grow@noorscale.com and a person will reply within one business day.
How much do managed AI agents cost?
It's a monthly retainer after a short onboarding audit. The fee depends on how many agents we look after, how much traffic they handle, how many systems they touch and how fast you need us to respond to incidents. Model and hosting usage stay on your own accounts. You'll get a written scope after a free call.
Can you manage an agent someone else built?
Yes, we start with an audit of the code, prompts, tools and recent conversations, then add tracing and tests around what's already there. If something is unsafe or fragile, we'll tell you what to fix first. We don't need to rebuild an agent to look after it.
How is this different from a normal software support contract?
A support contract fixes bugs when you report them. Agents also fail quietly, by giving a plausible wrong answer or calling the wrong tool, so they need regular conversation review, scored tests and model checks that ordinary support doesn't include. That's the gap this service covers.
How do you handle model upgrades?
We run the new model against your evaluation suite and compare accuracy, cost and speed with the current one. If it's better, we release it to a slice of traffic first and then widen it, with a quick rollback ready. If it isn't, we stay put and tell you why.
What happens when the agent breaks or gives a bad answer?
Alerts flag errors, failed tool calls and unusual patterns, and we follow an agreed runbook: pause the affected feature, roll back or route to a person. After every incident you get a short written review. The failure also becomes a new test case, so it doesn't repeat.
Do you need access to our customer conversations?
Yes, reading real conversations is how we find the failures that metrics miss. We review them inside your own accounts where possible, mask personal details where we can and follow the retention and access rules you set.
Which monitoring tools do you use?
We use tracing and evaluation tools such as Langfuse, LangSmith, Arize Phoenix and OpenTelemetry, and send alerts to the channels your team already watches. If you already have a monitoring stack, we work inside it.
What do you need from our team?
Access to the agent's code, prompts, logs and the systems it touches, plus one owner on your side who can answer business questions. Each month, that owner reviews the report and approves any change to what the agent may say or do.
Related services
What pairs well with Managed AI Agents?
- AI AgentsMulti-Agent SystemsCoordinated AI agents that run multi-step processes with approvalsExplore
- AI AgentsAI Chat AgentsWebsite, WhatsApp and social chat that qualifies leads and books callsExplore
- AI AgentsAI Governance & EvaluationEvals, red-teaming, guardrails and risk records for the AI you runExplore
- AI AgentsMLOps & LLMOpsPipelines, evaluation gates and monitoring that ship models and LLM apps safelyExplore
We build Managed AI Agents for teams in
Free 15-minute call
Tell us what you want to build.
In fifteen minutes you'll have a clear first step for Managed AI Agents and a plan to reach it. Pick a time below, or write if that's easier.
Prefer email? grow@noorscale.com
15 minutes with Noor
Noor Khan, Founder & CEO
You'll leave with
- Your biggest growth opportunity, in plain terms
- The first two or three moves worth making
- A clear plan for your first milestone
Noor listens first, then shows you the fastest route to the result you want.
Useful to have with you
- Your website address
- The goal or problem you want to solve
- Any numbers you already track
Booking opens on Cal.com. Or send a call request