Last week I was sitting in one customer review meeting, and the leadership team was very happy. They have built three agentic use cases. One for AI-SRE incident triage, one for FinOps cost optimisation, one for cloud operations. Pilot was successful. Demo was running. CFO was nodding his head. Everyone was talking about "scaling this across the enterprise."
But I already know what is going to happen in the next six months. I have done this kind of work in many banks, telecom companies, and tech firms. The same pattern is repeated again and again. Same story only.
The conversation around enterprise context layer, you can call it semantic layer, knowledge graph, ontology, RAG-plus, anything, has been fully captured by one narrative: accuracy. Better grounding. Less hallucination. Fewer wrong answers. All of this is correct, no doubt. But this is only one part of the story. And honestly speaking, not even the most important part when you start scaling.
Let me tell you what nobody is discussing.
The Cost Multiplication Problem
When you are taking a small PoC into production, one interesting thing is starting to happen. Your nice AI-SRE agent, which was handling 50 alerts in the pilot, is suddenly handling 5000 alerts per day. Your FinOps agent which was analysing one business unit is now scanning the entire cloud estate every hour. Each alert is not one LLM call. It is 8, 10, 12 tool calls. Every tool call is dragging the context. Every retry is doubling the cost. Every fallback to a bigger reasoning model is 10x the cost.
Now you multiply all this. Multiply across thousands of alerts per day. Multiply across 50,000 cloud resources. Multiply across 24x7 operations because incidents do not respect office hours. Multiply across agents which are calling other agents, which is clearly where we are going next.
The math is becoming very painful, very fast.
People are seeing this and saying "okay, we will use smaller models" or "we will do better caching" or "we will optimise the prompts". All these are good things to do. But they are treating only the symptom. The actual cause is sitting somewhere else.
Where is the Bloat Coming From?
See, the real problem is this. When your agent is not knowing the enterprise, it has to carry everything with itself. Every single time.
Take the AI-SRE agent. It is not knowing that "payment-svc" in your alerting system and "payments-service" in your deployment pipeline and "PaymentsService" in your CMDB are all the same thing. So what is it doing? It is pulling 15 documents to find out the meaning. It is doing three retrieval calls to remove the confusion. It is using the bigger reasoning model because smaller model cannot handle this kind of confusion.
The same SRE agent is not knowing that order-service is upstream of payment-svc and notification-svc. So when one slow query in order-service is firing alerts in three downstream services, the agent is investigating each alert separately. Three parallel root cause analyses, three sets of tool calls, three reasoning chains, for what is actually one single incident. Same problem, three times the cost.
The agent is also not knowing that "Karthik from platform team" is the owner of the payments domain, and during weekend the secondary owner is "Priya from SRE team". So it is doing entity resolution from zero every time, pulling HR data, calling directory tool, checking PagerDuty schedule, sometimes also hallucinating in between.
Now take the FinOps agent. It is not knowing that "ec2-prod-cluster-7" is tagged as production but is actually running dev workloads, because someone six months back did a quick fix and never updated the tags. So the agent is generating cost optimisation recommendations which are wrong, downstream review process is rejecting them, and the cycle is running again next week.
The FinOps agent is also not knowing the chargeback policies. It is not knowing which workloads have reserved instance commitments that should not be touched. It is not knowing that the EMR cluster which is looking over-provisioned is actually serving one quarterly regulatory batch which needs exactly that much capacity for three days every quarter. So it is figuring out all this every single time, with multiple reasoning steps and multiple tool retries.
This is what is killing your cost. Not the model. Not the framework. The missing context is killing you.
What Enterprise Context Layer Actually Gives You
Now imagine you have invested properly in building the enterprise context layer. Not just a vector database with chunks. I am talking about real organisation knowledge, properly encoded. Service catalogue with dependencies and ownership. Cost allocation policies. Runbook taxonomies. Reserved instance commitments. Deployment timelines. The actual map of how your enterprise is working.
What is changing when you have this?
First thing, retrieval is becoming precise. Instead of fetching 20 chunks and hoping the right one is there somewhere, you are fetching 2 or 3 chunks which you already know are relevant, because the context layer has done the resolution already. Your prompt size is dropping by 70 to 80 percent in many cases.
Second, tool calling is becoming intelligent. The SRE agent is not needing five tools to figure out which service owns the alert and who is on-call. The context layer is answering it in one lookup. The FinOps agent is not needing to scan tagging history to figure out actual workload type. It is already there in the context. Reasoning steps are reducing very much. The agent is no more behaving like a confused intern on first day. It is behaving like someone who is sitting in the company for ten years.
Third, and this part most people are missing, now you can actually use small language models at scale. SLM cannot reason through confusion. It needs clarity. When context layer is providing the clarity, SLM is working very nicely. For standard alert categories where runbook is already mapped, you do not need a frontier model. A 7B or 8B model is more than enough. You are replacing premium model calls for 60 to 70 percent of your workflows. Only the genuinely novel incidents, or the genuinely complex optimisation decisions, are needing the bigger model.
Fourth, latency is coming down. Token usage is coming down. Cost per interaction is coming down. And one more thing which nobody is calculating. Because the cost per interaction is down, you can do more interactions now. Use cases which were sitting in backlog as "not financially viable" are suddenly becoming viable. Continuous cost optimisation across the whole cloud estate, every hour, becomes affordable. Proactive incident detection, not just reactive triage, becomes possible.
One Real Example
We were working with one large fintech, and they had built one AI-SRE agent for incident triage. First version was very heavy. For every alert, the agent was pulling service inventory, dependency graph, recent deployments, on-call schedule, runbooks, log samples, metric history. Average triage was costing them around 45 cents in API calls and taking 25 to 30 seconds just for the initial response. With 2000 to 3000 alerts flowing through this agent every day, monthly cost was crossing $30,000 just for first-pass triage. And the on-call engineers were still complaining that recommendations are sometimes off.
We did not change the model. We did not change the framework. We spent two months building one proper context layer for SRE domain. Service catalogue with ownership and dependencies. Deployment timeline integration. On-call mapping. Alert taxonomy mapped directly to runbooks. Service criticality tiers. We connected the same properly to their existing CMDB and observability stack, not as separate RAG sitting on the side, but as the actual brain of the agent.
After this work, cost per triage came down to around 8 cents. Triage time came down to 4 to 6 seconds. And one more thing which even the customer was not expecting. We moved around 70 percent of standard alert categories to a much smaller model, because the context layer was telling the agent exactly which runbook applies, which dependencies to check, and who to notify. Only the genuinely novel or correlated incidents were needing the bigger model.
Yes, accuracy also improved. But the business case was made by the cost and latency story, not by accuracy. The CFO was not asking about hallucination percentages. He was asking about per-incident cost projection.
The same logic is now playing out for agentic FinOps. Without context layer, the FinOps agent is generating cost recommendations which are noisy, sometimes risky, often rejected by domain owners. With context layer, the agent is making recommendations which are pre-filtered for business context, already aligned with chargeback policies, and respecting commitment constraints. Recommendation acceptance rate is going up. Cost of running the agent itself is going down. Both sides of the equation are working in your favour.
Why is Nobody Talking About This?
I think there are two reasons.
One, the vendor narrative is dominated by model providers and framework companies. Their interest is to sell you more tokens, bigger models, fancier orchestration. They will not naturally tell you how to use less of their product. This is normal commercial behaviour, nothing wrong in it, but you must read the gap by yourself.
Two, the people who are building agents today are mostly coming from ML and prompt engineering background. They are thinking in terms of model behaviour, prompt patterns, evaluation metrics. The enterprise context layer is more of a data engineering and knowledge engineering problem. It is not exciting. It is foundational work. It is not giving you quick demo win to show in next Monday leadership meeting.
But I am telling you. I have seen this in BFSI, in telecom, in cloud-native fintechs, in large enterprise IT. The organisations which will actually scale agentic AI in SRE and FinOps and beyond are not the ones with the best prompts. They are the ones who are doing the patient, boring work of building the context layer properly.
What You Should Actually Do
If you are enterprise architect or AI leader and you are reading this, some practical suggestions from my side.
Start treating context layer as a first-class platform investment, not as a side project of one agent team. It is needing its own roadmap, its own team, its own SLAs. Otherwise every agent team will build their own version of it, and you will end up with ten broken context layers instead of one good one.
Stop measuring agent success only on accuracy and user satisfaction. Add cost-per-interaction, tokens-per-resolved-incident, percentage of workload which can run on small models, recommendation acceptance rate for FinOps agents. These are the metrics which will tell you whether you are actually scaling, or you are just doing a permanent demo.
Audit your existing agents. For each one, ask one simple question. How much of its work is figuring out enterprise context which should be already known? You will be very surprised how much waste is sitting there in plain sight.
And one last thing. Please do not get carried away by this model arms race. Bigger reasoning model is not the answer. Better context is the answer. The frontier models are very powerful, no doubt. But if you are using them to figure out which service owns an alert, or whether an EC2 instance is dev or prod, then you are paying premium price for a problem which should be solved at the data layer itself.
Closing Thought
The next 18 to 24 months will separate the enterprises which actually scaled agentic AI from the ones who got stuck in permanent pilot mode. The differentiator will not be the LLM they chose. It will not be the framework. It will be how seriously they have treated their enterprise context layer.
Accuracy is the marketing story. Cost-at-scale is the survival story.
Build accordingly only.