Select the appropriate architecture for each use case rather than applying multi-agent patterns by default:Deterministic workflow and state-machine architectures for repeatable, high-confidence operational processes.Planner-executor architectures for decomposing complex investigation, planning, and remediation tasks.Supervisor-worker or manager-worker architectures for coordinating specialist domain agents.Router architectures for selecting the right model, tool, knowledge source, workflow, or specialist agent.Reflection, critic, verifier, or judge patterns for quality assurance, validation, and safety checks.Event-driven architectures for responding to telemetry anomalies, workload failures, scheduler events, benchmark regressions, and operational alerts.Human-in-the-loop architectures for high-impact recommendations, privileged actions, or changes to production environments.
Focus on building algorithms for the extraction, transformation and loading of large volumes of real-time, unstructured data (marketing assets, brand collateral, campaign performance data, client media libraries) to deploy AI or ML solutions from theoretical data science models into production services.
Integrate the digital stack. Connect models and AI services with enterprise platforms, data sources, workflow systems, user interfaces and upstream or downstream applications.
- Local Services Search Team: Optimizes Local Service search experience, helping users discover catering, hotel, leisure and other life services. We apply LLM for query understanding, CTR/CVR prediction, local-service ai search card and agentic local service search framework.