Choose the Right Model When selecting an LLM for an agentic system, architects evaluate 5 key factors:
Reasoning Capability: Does the task need deep logic, or just simple classification?
Performance/Latency: Does the application require extremely fast responses?
Cost Considerations: Is the model affordable to run at scale (inference and token costs)?
Data Privacy: Does the data need to remain on-premise without touching external clouds?
Enterprise Integration: How well does the model fit into the existing workflow?
There is No Single Best Model A "one-size-fits-all" model does not exist. Instead of seeking a universal best model, modern AI systems use a Combined Pipeline (Model Stack). For instance, smaller models handle cheap, fast tasks like routing and classification, while large, expensive models are reserved strictly for deep reasoning and planning.
Based on the architectural principles outlined above, the key is to balance cost, speed, and intelligence.
For the below example architectural flow, lets identify the Model for each of the AI Agents.
1. Smaller, Fast Models (e.g., Llama 3 8B, Claude Haiku, GPT-4o-mini)
These models are used for "cheap, fast tasks like routing and classification." In the architecture, they handle structural, repetitive, or low-complexity tasks where speed is critical and the risk of hallucination is managed by strict constraints.
The Orchestrator / Router: Before any deep thinking happens, the stateful LangGraph orchestrator needs to classify the user's intent (e.g., "Is this a risk query or a daily status update?"). A small, fast model can instantly classify the intent and route the request to the correct agent
. Integration Analysts (Jira & ServiceNow Agents): These agents have a single, dedicated job: extract structured signals from external tickets
. Because this task is purely data extraction and mapping (often into JSON schemas), a small model is perfectly capable and significantly cheaper . The Communication Agent: This agent's job is to take raw technical logs and dependencies and translate them into a clear, easy-to-read summary for stakeholders
. Summarization is a standard capability that small models handle exceptionally well at low latency. Governance & Evaluator Agents (Initial Pass): Small models can act as fast classifiers to check if an output violates a corporate policy, contains Personally Identifiable Information (PII), or fails a basic compliance rule before passing it along
.
2. Large, Expensive Models (e.g., GPT-4, Claude 3.5 Sonnet)
These models are reserved strictly for "deep reasoning and planning." They are invoked only when the system needs to process ambiguity, synthesize complex contexts, or make high-stakes decisions.
The Planner Agent: When a Program Manager asks a vague question like, "Look into the next sprint and tell me the risks," the Planner Agent must break that ambiguous request down into a logical sequence of actionable steps without executing anything
. This requires high-level cognitive planning, making it the perfect job for a large model. Risk & Dependency Agents: These agents run parallel analyses to figure out "what could go wrong," detect hidden upstream/downstream blockers, and evaluate cross-team impacts
. Identifying hidden correlations across hundreds of Jira tickets requires the deep, sophisticated reasoning capabilities of a massive frontier model. The Action Agent: This agent safely executes tasks, but only after synthesizing all the risk analyses and dependency checks
. Because its decisions have actual consequences, it requires the high accuracy and reasoning power of a large model.
The Architectural Takeaway
By designing the system this way, the architecture protects operational costs. The system uses a cheap $0.10/million token model to pull the Jira tickets, format the data, and summarize the final output, and only pays the premium $10.00/million token cost for the specific nodes (Risk, Dependency, Planner) that actually need to "think" about the data.
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