Token Budgets And Build Pipelines: Engineering AI Systems That Do Not Break The Business

Effective management of token budgets and robust build pipelines are crucial for maintaining the economic stability of AI systems as they scale. This article discusses strategies to prevent cost overruns and ensure predictable performance.

Enterprises rarely notice when an AI system begins to drift. The prototype behaves well, the first demo feels effortless, and the early cost numbers look harmless enough to ignore. It is only after the system meets real users—longer conversations, inconsistent prompts, unpredictable branching, multi-agent exchanges—that the architecture reveals what it actually is. Costs climb quietly. Context swells without warning. Routing becomes erratic. Tool calls multiply in places no one monitors. Nothing about this resembles the clean, predictable runs teams saw during development.

This gap between demo economics and production economics is now one of the largest constraints in AI adoption. Teams do not lose control because they lack skill; they lose control because early signals are misleading. Token budgets behave like latency budgets: once the shape of the system is set, everything downstream is reactive. If the architecture permits itself to grow unchecked, it will. If it is built with boundaries, it holds its line. To unpack how this tension plays out in systems that operate at a global scale, we spoke with Chirag Agrawal, Senior Engineer and Tech Lead for the Alexa+ AI Agent at Amazon who works on the interaction layers users never see: long-context memory, token-aware compression, deterministic orchestration flows, and the build-time contracts that keep Alexa’s AI behaviour stable even as usage surges. As a Program Committee Member and reviewer for the ICTAI Conference 2025, he evaluates AI systems not on novelty but on whether they maintain discipline under stress. That same lens shapes how he thinks about cost governance, durability, and the engineering choices that prevent small inefficiencies from becoming system-level liabilities.

Why do teams still postpone token-cost planning, even though it determines whether an AI system survives real traffic?

Thanks for having me today. In the early stage, the system gives you a very misleading picture. Everything is small—short prompts, shallow memory, almost no retries. Nothing has been stressed long enough to show its real behaviour. That creates this comfortable belief that cost is something you can “optimise later.” The reality is that token behaviour is baked into the architecture from day one. Once users start having long conversations, once fallback logic starts firing in real situations, and once an agent misroutes under load, you begin to see how quickly those small design choices compound into major expenses. This is usually the moment where teams get caught off guard. The prototype behaves like a well-mannered version of the actual system, so people assume production will follow the same pattern. It never does. As memory builds and routing becomes more exploratory, token usage grows faster than teams can react to it. By the time cost becomes a topic of conversation, the system has already learned behaviours that are very hard to undo. The drift was already there; you just could not see it in the beginning.

From your work on the Alexa Build Platform, which principles translate most directly into keeping LLM systems cost-bounded and predictable?

For me, the Alexa Skill Build System was where we took uncertainty off the table. At Alexa’s scale, we could not rely on a pipeline that simply assembled artifacts and hoped the runtime behaved. The build system compiled each skill into a dependency graph, produced immutable and versioned outputs, and enforced manifests, quotas, and contracts before anything shipped. That gave us a clear view of what each step would do, what it would cost, and how it would behave under load. Whenever build-time behaviour drifted, runtime behaviour drifted with it, so determinism and strong contracts were non-negotiable. I apply the same principles to our LLM-based multi-agent and memory platforms for Alexa+. Each customer request is treated as a pipeline with defined steps and token budgets, not a free-form call to a model. Prompts, models, retrieval indexes, and post-processing logic are versioned artifacts, which means any change in cost or behaviour can be traced and rolled back. Product teams declare their needs in configuration, while the platform selects the model tier, applies global limits on tokens per request or tenant, and enforces consistent latency, quality, and price contracts. People often assume the model is the hardest part to control, but most instability comes from orchestration. If your prompt envelope is unstable, token counts are unstable. If routing decisions shift under load, costs swing with them. If your SDK lets every team interpret contracts differently, the ecosystem fragments. By making the orchestration path as deterministic as the build system and giving it clear SLOs for cost, latency, and reliability, we keep LLM systems predictable as they scale instead of discovering at month-end that the platform has quietly reinvented itself.

Where do the most damaging hidden costs accumulate inside LLM systems, and how can we build pipelines to surface them before deployment?

The most damaging hidden costs almost never start where people expect. In my experience, they live in the orchestration layer, not in a single expensive model call. You get memory residue from earlier turns that no one accounted for. You get redundant agent calls that slip through because the branching logic looked harmless during development. You get fallback loops that seem protective on paper but expand token usage every time they fire. Under cognitive strain, tool call patterns multiply in ways that are easy to miss until the bill arrives. Long context memory is usually the biggest offender. One conversation feels fine in isolation. Then you see a weekend spike of similar conversations, and suddenly it becomes a cost event. Multi agent systems amplify this, because every agent inherits context that might not even be relevant anymore. If you are not inspecting what gets carried forward, you end up paying repeatedly for information the system no longer needs. The same pattern appears with retrieval fanout and retries: each extra document or extra pass looks innocuous, but multiplied across traffic, it becomes the dominant cost. For me, the build pipeline is the place where you catch all of this before it becomes normalised. That pipeline should run representative traces through the system, measure token use at each step, and surface patterns like prompt inflation, volatile routing, or unbounded memory growth before anything ships. If you can see how context and tool calls evolve over ten or twenty turns in a test run, you can intervene while the behaviour is still changeable. In my experience, any variability you see in development grows louder in production. The pipeline’s job is to reveal that variability early enough that you can correct it, so cost discipline becomes part of the design, not an emergency reaction after deployment.

What early signs tell you whether an AI prototype will remain economically stable once it meets real user traffic?

Under the hood, economic stability always comes back to three surfaces: which model tiers you actually use, how many input tokens you send, and how many output tokens you allow. Most cost surprises are just these three moving quietly. The early signals I look for are proxies for how those surfaces will behave under real traffic. Prompt stability is the first one. If similar requests produce very different prompt shapes, your input token count is already unstable. That usually means context assembly, tool outputs, or templates are drifting. Prompt volatility always becomes cost volatility because the system is feeding an unpredictable number of tokens into a fixed-priced model. At scale, that variability multiplies. Routing coherence is the second signal. In multi-agent or multi-model systems, cost is not just the unit price of a model; it is how many times you call it and which tier you escalate to. Coherent routing means most requests follow predictable paths. Scattered routing leads to uncontrolled inference: extra hops, larger fallback models, and multiple agents reading the same context. If routing is unstable before scale, it will not stabilise after. The third being summarisation integrity; long-context systems fail here most often. A single conversation feels harmless, but as sessions stretch, weak summarisation quietly inflates input tokens. You keep paying for paragraphs that no longer add value. That bloat becomes a permanent cost anchor. If compression degrades as conversations grow, cost will climb even if traffic stays flat. When I look at prototypes, I instrument these three directly—effective model tier usage, average input tokens, and average output tokens—sliced by route and conversation length. If those curves are noisy in a controlled environment, they will only get noisier in production. In my role as a Judge for the Herizon Business Intelligence Awards, the systems that stood out were not the most complex ones, but the ones that showed early stability across these exact surfaces. Sophistication is optional but stability is not.

Multi-agent systems can become unpredictable under load. What guardrails matter most for keeping them from triggering uncontrolled inference?

A multi-agent system without boundaries behaves a lot like a well-meaning group where everyone tries to help even when they should not. The fix is not to weaken the agents but to narrow the conditions in which they are allowed to act. Orchestration has to decide who participates, how much context they see, and exactly when escalation is appropriate. Memory visibility is a big part of that. Agents should not inherit the full context just because it is available. They should see only what their role demands. When agents are overloaded with information, they stretch their reasoning, produce longer outputs, and drift into paths that inflate cost. That drift is subtle, but it compounds quickly at scale. Deterministic delegation is the other guardrail that keeps everything in check. If two similar requests trigger completely different agent paths, the system has already lost predictability. Orchestration needs to enforce policies that keep behaviour consistent unless there is a very explicit reason to deviate. This is not about making the system rigid; it is about making sure the cost envelope is driven by rules rather than by improvisation.

What principle ultimately allows an AI system to scale without drifting into unsustainable token and cost patterns?

The principle is simple. Systems need to tighten as they scale, not loosen. Teams convince themselves that adding more context or widening visibility will improve reasoning. It never holds. Those choices create cognitive debt that looks harmless in development and becomes destructive under real traffic. This is the same argument I made in my Hackernoon article, “When Context Becomes a Drug: The Engineering Highs and Hangovers of Long-Term LLM Memory.” Long-term context feels good early because answers look sharper. Over time, the model becomes dependent on larger histories, carries residue from earlier turns, and loses the ability to downsize without breaking quality. That is how drift becomes permanent. A scalable system fights that instinct. It summarises more aggressively as the conversation grows. It narrows the set of active agents as the task becomes clearer. It enforces stable prompt shapes instead of letting every feature create its own logic. These constraints do not reduce intelligence; they keep the system functional when the workload spikes.

The conclusion is direct. An AI system that protects itself from drift protects the business using it. Systems fail not because they lack capability, but because they ignore boundaries. The systems that survive are built by teams that do not.