AI Context & LLM Optimization
Making the use of large language models (LLMs) and agents efficient: context reduction, data cleansing, tool selection, and cost control
An agent should see only what it needs to decide. Everything else is noise that costs tokens and degrades reasoning. Many companies have agents and LLM applications that work but consume too much. The problem is rarely the model. It is almost always the way context, tools, and data are fed to the LLM. That is exactly where we work, while maintaining or improving decision quality.
The service in brief
- Service
- Technical optimization of LLM and agent usage: context, tools, data, and costs.
- When you need it
- When agents work but cost too much or are inefficient.
- R.O.C.E. phase
- Optimize: the second phase of the Miriade R.O.C.E. methodology (Recover · Optimize · Control · Evolve).
- Includes
- LLM cost control (AI FinOps at agent level).
- Prerequisite
- Agents or LLM applications already in production. A good second engagement after AI in Production.
- Output
- Context and cost assessment, optimized architecture, tool/MCP selection, system prompts and budgets, observability, estimate and measurement of savings.
- Related
- AI in Production Enablement, MirAI, and AI-Ready Data & RAG (noise that originates in the sources).
The question is not "which LLM do we use?" but what we feed it
The right question is not "which LLM do we use?" but what actually enters the agent's context, with which tools, which data, and which limits. That is where costs and inefficiencies concentrate.
From overloaded agent to efficient agent
Before — inefficient
After — optimized
How it works
We start from the actual behavior of agents and applications. Only then do we define the optimization actions, always starting from the points with the greatest impact on costs.
Triage and scoping
We identify which agents and LLM applications are involved, which cost or efficiency issues are emerging, and which objectives to pursue.
Assessment
We analyze context, operating loop, tools, payloads, models, and costs, and identify issues, priorities, and quick wins.
Designing the changes
We define context reduction, tool selection, prompt optimization, model heterogeneity, and operating budgets.
Progressive implementation
We proceed through controlled changes, starting from the points with the greatest impact on costs.
Measurement
We compare costs, tokens, performance, and quality before and after the changes.
Continuous observability
We introduce monitoring and treat prompts and configurations as elements that evolve over time.
The levers we work on
The scope adapts to your agents and cost objectives. The main areas:
AI Context Assessment
Map of the context and operating loop, identification of information and steps that add nothing to reasoning, analysis of exposed tools, quick wins.
LLM Cost & FinOps Assessment
Cost breakdown by model, agent, and stage, tool bloat detection, payload analysis, and estimate of expected savings.
Context & Loop Re-architecture
Data extraction and cleansing moved outside the loop, preprocessing with flash models, loop reduced to reasoning only.
Tool & MCP Optimization
Fewer exposed tools, targeted selection of MCP tools, and optimization of the interface between agent and tools.
System Prompt & Payload Engineering
Efficient system prompts, instructions on tool use and budgeting, prompts treated as dynamic playbooks.
Custom Tool Development
Purpose-built tools optimized for how the LLM uses them: data cleansing and trimming, summarization, specific low-cost functions.
Observability & Computational Heterogeneity
Observability with dedicated tools (e.g., LangSmith), cost and efficiency metrics, and the right model assigned to each stage.
We do not just measure spending: we cut it
Miriade does not address LLM efficiency by changing the model or tweaking a prompt. We work on the entire way context, data, and tools are fed to the agent. Technical optimization and cost control (AI FinOps) go together: here, we do not stop at measuring spending. We cut it by acting on the cause.
The problem is rarely the model. It is almost always what we feed it.
Where it fits in the AI path
AI Context & LLM is the deepest technical engagement on agents and costs. It works alongside AI in Production and MirAI, and leads back to AI-Ready Data & RAG when the noise originates in the sources.
AI-Ready Data & RAG
We prepare data and sources for AI and build the first solution.
AI in Production
We stabilize and govern AI that is already in production.
AI Context & LLM
We optimize the context, tools, and costs of agents and LLMs.
You are hereMirAI
Continuous monthly managed service for your AI system.
Frequently asked questions
Do you change the model or tweak the prompts?
No. We work on the entire way context, data, and tools enter the agent, not on a single prompt or model.
How is it different from AI in Production Enablement?
AI in Production measures and governs spending at the solution and operations level. AI Context & LLM cuts it by working technically on the agent: context, tools, and models.
Isn't MCP enough to optimize tools?
MCP connects tools to the agent, but it does not ensure optimization for the specific use. Exposed tools still need careful selection.
When does it make sense to build custom tools?
When costs remain high after all limits and optimizations are in place. A purpose-built tool, optimized for how the LLM uses it, can reduce them further.
Do you need agents already in production?
Yes. It is a technical engagement for organizations that already have agents or LLM applications in use with cost or efficiency issues. It is often a good second engagement after AI in Production.
How does it start in practice?
With a context and cost assessment: what enters the agent, which tools are exposed, and where spending concentrates. From there, we define the actions and priorities.
The technologies we use
Some of the technologies and platforms we work with on this service.
Do you want to reduce the costs of your LLM agents without losing quality?
We start from the context, tools, and models you actually use, and build a plan to lighten the agent, remove the noise, and keep costs under control.
Tell us about your LLM agents
A few details about your agents and costs are enough. We will contact you for a call and define the scope of the context and spending assessment.
Privacy and security note. Do not enter credentials, passwords, or sensitive data in the form. Access, logs, and technical information will be handled later through agreed secure channels.
