The Production AI Buyer's Guide: How to Evaluate AI Agents for Production Operations
A comprehensive buyer's guide for evaluating AI production agents covering vendor questions with benchmarks, red flags, POC methodology, and outcome benchmarks from real deployments.
How to evaluate AI agents for production operations: what to look for, what to avoid, and how to run a POC that actually tells you something. Covers vendor questions with benchmarks, red flags, and real deployment outcomes.
We were promised speed with microservices. Instead, we got a lot of operational overhead. We smashed the monolith but kept using the same old tools to watch the pieces. Now you have a small team trying to manage a mountain of software. The brain power needed to debug one single request is more than one human can handle.
The bottleneck shifted. It is not about how fast you merge to main anymore. It is about how long you survive in production before the pager screams. Production support is a software problem. We should treat it like one. This guide covers what to look for, what to dodge, and how to run an evaluation that isn't a waste of time.
What Came Before
We have spent fifteen years building a stack to handle outages. But we kind of stalled out when it came to the intelligence layer.
Gen 1 was the SaaS era. Datadog, New Relic, PagerDuty, Opsgenie. Lots of dashboards and tickets. They told us "what is happening" and "who needs to wake up." We got logistics, but a human still had to do all the actual thinking and fixing.
Gen 2 brought us agentic tools. AIOps, AI SRE, AI DevOps. They tried to answer "why is this happening?" They helped with root cause and connecting the dots. But they stopped at investigation. They didn't actually fix anything or handle the full lifecycle.
Gen 3 is agentic systems. Real AI production agents. They handle the whole mess of running code in production. Incident response, triage, CI/CD, and finding bugs before they bite. You keep your monitoring and incident management. You just stop using your senior engineers as highly paid human routers.
What Production Agents Should Do
Before you talk to vendors, get your head straight on what this stuff should actually do:
Investigate across the full stack
Production fires rarely politely stay inside one tool. An agent looking at only one vendor's telemetry is going to miss the actual fire while staring at the smoke.
Triage alerts and separate signal from noise
Find the monitors someone set up at 3 AM and forgot about. Kill the duplicates. Only wake a human up if a human is actually needed.
Answer internal questions
"How does service X talk to service Y?" That question alone costs your senior engineers about ten hours of focus time every week.
Take action with guardrails
Roll back the bad deploy. Restart the service. Quarantine that test that has been flaky since Tuesday. Open fix PRs. Just make sure there is an approval button before it deletes the database.
Learn from every incident
Read the Slack threads, the old incident reports, and the docs nobody updates. It should get smarter on its own without needing a dedicated teacher.
Keep CI/CD moving
Flaky tests and mysterious build failures block the merge queue and kill velocity. A production agent should detect them, quarantine the offenders, and open fix PRs so deployments actually deploy.
Work proactively, not just reactively
Find the hidden gremlins, the weird cost spikes, and the reliability risks before they turn into a full-blown incident.
Six Questions to Ask Every Vendor
Ask these questions during evaluations. The answers will tell you who has a real tool and who just has a nice slide deck.
1. How long to first value?
Some vendors want weeks just to say hello to your infrastructure. That is too long. The good stuff integrates in hours. You should see a real investigation the same day you turn it on.
Ask the vendor: How many hours between signing the contract and the first real production investigation? What integrations do I have to hook up before this thing starts paying rent?
Benchmark: Good tools integrate in hours and handle a first investigation the same day. If a vendor says they need weeks, ask them what exactly they are doing.
2. Does it work across my full stack?
If the tool only reads data from one vendor, it is wearing blinders. That is a problem because the issues that ruin your weekend are almost always cross-system.
Ask the vendor: How many data sources does the agent query natively? What happens when the root cause lives in a tool you do not integrate with?
Benchmark: Throw a real incident at it. One that needed data from three or more tools to solve. If it cannot connect the dots, it is just a dashboard with a chat box attached.
3. Does it take action or just recommend?
Investigation without fixing anything is just glorified sightseeing. Your team still has to do the work. Ask them to prove they can actually execute actions in production.
Ask the vendor: Show me a fix you actually ran in production. Not a demo. A real customer example. And what approval workflows do you have so it does not delete the database by accident?
Benchmark: Ask to see a log of actions the agent has actually executed in the wild. If remediation is listed as a future capability, that tells you everything you need to know.
4. Can I see and edit what it knows?
Black boxes are great for airplanes but terrible for production trust. You need to see the receipts. The evidence chain, the reasoning, and where it learned what it thinks it knows.
Ask the vendor: Can I see everything the agent thinks it knows about a service? Can I edit, fix, or remove entries when it gets confused?
Benchmark: Ask to see the agent's knowledge graph for one of your services. If they cannot show it, or if it looks like a black box, your senior engineers are going to laugh at it.
5. What does it do on a quiet day?
Tools that only handle incidents sit around doing nothing 95% of the time. Look for something that works for a living: proactive discovery, CI/CD support, internal Q&A, and capturing knowledge.
Ask the vendor: What does the agent do on a quiet Tuesday? What is the daily active usage at your reference customers?
Benchmark: Test the agent during a quiet week. If usage drops to zero when nothing is broken, you are paying for an expensive insurance policy. You want a daily tool.
6. What is the security posture?
If you are in a regulated industry, you need on-prem or VPC. No exceptions. If a vendor is cloud-only, ask them exactly how they plan to touch your sensitive telemetry without setting off alarm bells.
Ask the vendor: Do you offer on-prem or VPC deployment? What compliance certifications do you have? Does any production data actually leave our environment?
Benchmark: If you need on-prem, make sure it is a real option. Do not let them sell you a roadmap item that requires a custom contract.
Red Flags to Watch For
You know within ten minutes. If they show you slides or a canned video instead of a live investigation, hang up. They are showing you the happy path because the product probably explodes when it touches real data.
Watch out for the heavy lift. If your environment is simple but they still need to send "forward-deployed engineers" or say the trial takes six weeks, you aren't buying software. You are buying expensive consultants wearing a platform costume.
Grill them on the reasoning. If they say the system "just magically learns" but can't show the actual evidence of what the agent learned, it is a black box. In production, a black box is not a solution. It is a liability waiting to ruin your sleep at 3 AM.
How to Run a POC That Actually Tells You Something
Most POCs fail because the scope is garbage. Here is how to set one up so the results are actually useful:
- Scope to one or two teams. Pick the team that looks tired. The ones with high alert volume and heavy on-call. Do not test this on a quiet service that has not paged anyone since 2019.
- Define metrics before you start. Measure MTTR before and after. Track time to root cause, page volume, and how grumpy the engineers are. Also count the hours wasted on "quick questions."
- Run on real incidents for 2-4 weeks. Staged demos are nice theater. But they tell you absolutely nothing about how the agent will handle your terrifyingly complex spaghetti code.
- Measure time to first value. If it takes weeks to see a single investigation, write that down as a red flag. That is too slow.
- Do the team like it?. It's developer tooling at the end of the day. Do the devs absolutely love it?
What Good Looks Like
When you look at vendors, measure them against these numbers from real production deployments:
- Look for a 40% drop in MTTR that actually lasts
- Root cause in under 10 minutes instead of the usual 60-90
- Saving thousands of engineering hours each year
- Answering 80% of routine questions without bugging a senior dev
- Catching problems before the pager screams
- Value on day one
- 90%+ engineering satisfaction
These are real stats from companies with messy microservices and stressed-out SRE teams. If a vendor cannot show you similar numbers from actual customers, keep walking. When you are ready to talk budget, read about what production agents actually cost.
Frequently Asked Questions
How much should an AI production agent cost?
Datadog Bits AI SRE charges $25/investigation, and many vendors have followed suit. We recommend budget about the same per engineer as you would with coding agents like Cursor or Claude.
What is the difference between an AI production agent, AI SRE, and AIOps?
AIOps was generation one: anomaly detection and pattern matching. AI SRE was gen 2: read-only investigations. AI production agents are gen 3: agents that handle the whole end to end after code merges, including bugs, investigation, alerts, CI/CD, internal support, and fixing things across the stack.
How long should an AI production agent POC take?
Two to four weeks. Scope it to one or two teams that are actually hurting. Define success metrics before you start. The tool should show value on real incidents in the first week.
What is the most important thing to test during an evaluation?
Throw a real incident at it. Not a demo, not a synthetic test. Use a past incident that required data from three or more tools to resolve. If the agent cannot connect the dots across your actual stack, it will not help when it matters.
How do I get buy-in from my platform team for evaluating an AI production agent?
Start with the skeptics. Pick your hardest core engineering team for the POC. If you can win them over after a few weeks of real fires, everyone else will be bought in. Transparency is everything here. They need to see the evidence chain or they will not trust it.
What integrations does an AI production agent need?
At minimum, you need your observability platform (logs, metrics, traces), source control, and Slack. But the more context the agent has the better. Hook up CI/CD, incident management, and internal docs if you want it to actually investigate well.
Can an AI production agent work with my existing incident management tools?
Yeah, they play nice together. Production agents sit alongside PagerDuty, Rootly, Firehydrant, and Incident.io. Those tools manage the logistics of waking people up. The agent provides the brains. It tells you what broke, why, and how to fix it.
Ready to Evaluate?
TierZero Production Agents integrate in one hour. You see value on real incidents in the first week. No armies of consultants. No setup that takes a month. And definitely no black boxes.
Co-Founder & CEO at TierZero AI
Previously Director of Engineering at Niantic. CTO of Mayhem.gg (acq. Niantic). Owned social infrastructure for 50M+ daily players. Tech Lead for Meta Business Manager.
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