Choosing the Right Model for Your AI Agent

Selecting a model is no longer a binary choice between “best” and “cheapest.” Teams build cascades, balance latency against accuracy, and satisfy legal requirements across multiple regions. Use this framework to align stakeholders before negotiating with vendors or provisioning GPUs.

Step 1: Capture constraints and success metrics

Start with the “jobs to be done.” What user task needs automation? How accurate must the agent be? What latency keeps the experience delightful? Translate those answers into measurable KPIs: resolution rate, CSAT improvement, time-to-reply, or engineering hours saved. Document hard constraints like data residency, security certifications, and acceptable model training data sources.

Step 2: Build a scoring matrix

Create a matrix that lists candidate models versus evaluation categories. Typical columns include:

• Capability: benchmark or custom eval scores tied to your KPIs.
• Latency: P95 response times under expected traffic.
• Cost per success: Use the calculator to normalize.
• Compliance: certifications, data retention policies, and logging controls.
• Integration effort: SDKs, tool calling, retrieval integration, and support tiers.

Weight each column based on stakeholder priorities. Finance might emphasize cost while customer experience leaders prioritize quality metrics.

Step 3: Design cascades intentionally

Most successful teams deploy cascades: start with a lower-cost model for the majority of traffic, escalate to a premium model when confidence drops, and fall back to human review for edge cases. Document decision rules clearly: confidence thresholds, latency budgets, and task categories that bypass cheaper tiers.

Example cascade:

1. Llama 4 70B instruct for simple FAQs and classification.
2. Claude 4.5 Sonnet for complex reasoning or sensitive tone requirements.
3. Human escalation when compliance keywords trigger or latency exceeds the SLA.

Step 4: Engage procurement and legal early

Bring your matrix and cascade plan to procurement before vendor outreach. Highlight what data flows through the model, whether you need zero-retention modes, and how you will monitor abuse. Legal teams typically request information about training data provenance, indemnification, and incident response timelines—capture those in a checklist to shorten contract cycles.

Step 5: Operationalize the decision

Once you downselect, set up dashboards that track the metrics you used in the evaluation. If cost per success or accuracy drifts, you can revisit the matrix and adjust cascades quickly. Pair this with the deployment and observability playbook to keep stakeholders aligned.

Re-run the evaluation quarterly. Pricing shifts, new checkpoints release, and your product evolves. Keeping the matrix current ensures future roadmap pitches start from data, not anecdotes.