The Problem
Your team needs to pick a new database, frontend framework, or hosting provider. Everyone has opinions, but nobody has time to do a thorough comparison. The CTO asks for a structured evaluation, and the senior engineer spends a week reading docs, blog posts, and benchmark articles — then produces a Google Doc that's already outdated. Meanwhile, the team argues based on gut feeling rather than data.
The Solution
Use web-research to gather current benchmarks and community sentiment, competitor-alternatives to map the landscape, data-analysis to score options against your criteria, and report-generator to produce a professional evaluation document.
Step-by-Step Walkthrough
1. Define your evaluation criteria
Tell the agent what you're evaluating and what matters:
We need to choose a message queue for our event-driven architecture. Compare RabbitMQ, Apache Kafka, Amazon SQS, and Redis Streams. Our criteria: throughput (>50k msgs/sec), latency (<10ms p99), operational complexity, cost at our scale (500M messages/month), and ecosystem maturity.
2. Research each option
The agent searches for recent benchmarks, documentation, pricing pages, and community discussions for each technology:
Research complete. Sources analyzed:
- 12 benchmark articles (2024-2025)
- Official documentation for all 4 options
- 8 production case studies at similar scale
- Pricing calculators for managed offerings
- GitHub activity and Stack Overflow trends
3. Review the scoring matrix
Tech Stack Evaluation — Message Queue
| Criteria | Weight | Kafka | RabbitMQ | SQS | Redis Streams |
|---|---|---|---|---|---|
| Throughput | 25% | 9/10 | 6/10 | 7/10 | 8/10 |
| Latency (p99) | 20% | 7/10 | 8/10 | 6/10 | 9/10 |
| Operational cost | 20% | 4/10 | 6/10 | 9/10 | 7/10 |
| Monthly cost | 20% | $840 | $620 | $1,250 | $480 |
| Ecosystem maturity | 15% | 9/10 | 8/10 | 7/10 | 5/10 |
| Weighted Score | 7.1 | 6.8 | 7.2 | 7.2 |
4. Get the detailed recommendation
Give me the full recommendation with trade-offs for each option and your top pick for our use case.
5. Generate the structured report
The agent generates a complete evaluation document with an executive summary, detailed analysis per option, risk assessment, migration considerations, and a final recommendation with reasoning — ready to share with stakeholders.
Real-World Example
Tomás is the engineering lead at a 30-person e-commerce startup. They've outgrown their PostgreSQL-for-everything architecture and need a dedicated search solution. Using the tech stack evaluation workflow:
- Tomás asks the agent to compare Elasticsearch, Typesense, Meilisearch, and Algolia for product search with 200k SKUs
- The agent researches indexing speed, query latency, relevance tuning options, hosting costs, and developer experience for each
- The scoring matrix shows Typesense leading on cost and simplicity, Elasticsearch on features, and Algolia on managed convenience
- The final report includes a recommendation to start with Typesense for cost-efficiency, with a migration path to Elasticsearch if advanced features are needed later
- Tomás presents the report at the architecture review — the team makes a decision in one meeting instead of debating for three weeks
Tips for Better Evaluations
- Weight criteria before researching — deciding what matters after seeing results introduces bias
- Include operational cost, not just license cost — a free tool with high maintenance overhead costs more than a paid managed service
- Check community health — GitHub stars mean less than recent commit frequency, issue response time, and contributor diversity
- Test with your actual data — benchmarks from blog posts rarely match your specific workload
- Set a decision deadline — evaluation paralysis is real; time-box the research to one week maximum
- Document the decision — future team members will ask "why did we pick this?" and the evaluation report is your answer