Agents without well-designed memory lose track of multi-step tasks, repeat actions, and contradict themselves across a session. Vervelo designs the full memory stack for each agent: working memory (active task state and recent tool outputs), episodic memory (history of actions taken within a session), semantic memory (retrieved knowledge from your knowledge base), and long-term user or patient memory (persistent facts across sessions). Memory architecture directly affects both task completion accuracy and token cost — we model both dimensions before selecting an approach and implement summarization pipelines that keep context manageable without discarding task-critical information.

Clinical AI agents have requirements that general-purpose agent frameworks don't address: HIPAA-compliant tool call logging, PHI handling in memory and retrieval, clinical safety guardrails that prevent the agent from taking autonomous actions outside defined safe boundaries, and audit trails for every agent decision. Vervelo has built production agents for prior authorization processing, clinical documentation automation, care gap identification and outreach, appointment scheduling, revenue cycle workflow automation, and patient-facing support — each with healthcare-specific safety constraints, escalation logic, and compliance controls built into the agent design, not bolted on afterward.

Multi-agent systems fail when agents contradict each other, duplicate work, or wait indefinitely for outputs that never arrive. Vervelo designs the inter-agent communication protocol — the data contract for what agents pass to each other, the shared state store that all agents read from and write to, the consensus mechanisms for resolving conflicting agent outputs, and the timeout and retry logic that prevents livelock and deadlock in multi-step pipelines. We also design the human-in-the-loop checkpoints: the points in a multi-agent workflow where an autonomous decision is high-stakes enough to require human approval before execution.

Vervelo selects and implements the orchestration framework that matches your workflow's complexity and reliability requirements. For graph-based workflows with explicit state machines, we use LangGraph. For role-based collaborative multi-agent systems, we use CrewAI or AutoGen. For simpler sequential pipelines, we use LangChain LCEL or direct API composition. For high-reliability, custom-control workflows — particularly in clinical contexts where off-the-shelf framework abstractions introduce unpredictable behavior — we build custom orchestration layers. Framework selection is driven by the control and observability requirements of your specific use case, not by which framework is trending.

AI systems require a different CI/CD approach than traditional software. Vervelo builds AI-specific deployment pipelines that run the full evaluation suite — prompt regression tests, agent behavior tests, integration tests against live tool endpoints — before any change can be promoted to production. Deployment is canary-based with traffic splitting: new agent versions receive a small percentage of traffic while metrics are monitored, with automatic rollback if performance drops below defined thresholds. For healthcare AI, promotion gates include clinical safety checks: no agent version that produces outputs matching unsafe content patterns can be deployed, regardless of other performance metrics.

Production AI systems without observability are black boxes. Vervelo instruments every AI deployment with full distributed tracing across the agent decision loop — capturing every LLM call, every tool invocation, every retrieved document, and every inter-agent message with latency, token count, and cost attribution. We integrate LangSmith, Helicone, or custom tracing backends depending on your stack. Dashboards surface the metrics that matter: task completion rate, mean time to resolution, per-workflow cost, tool error rates, and output quality scores from automated evaluation. Alerting fires on cost anomalies, latency spikes, safety violations, and quality degradation — before users notice.

When your LLM application needs to reason over proprietary knowledge — clinical protocols, payer policies, patient history, formulary data, billing rules — the application architecture must include a reliable retrieval layer. Vervelo builds production RAG applications with the full pipeline: document ingestion and preprocessing, chunking strategy optimized for your document types, embedding model selection and management, vector store setup and index optimization (Pinecone, Weaviate, Qdrant, pgvector), hybrid retrieval combining dense and sparse search, reranking for precision, and retrieval quality evaluation. We also build the application-layer logic that selects retrieval strategy dynamically based on query type, manages freshness for time-sensitive data, and handles multi-source retrieval from heterogeneous knowledge bases.

LLM applications that feed into downstream clinical systems — EHR documentation, billing workflows, care management platforms — require reliable structured output. Vervelo implements JSON schema binding, function calling, output parsers, and validation layers that enforce the exact data structure your downstream systems expect. We handle the edge cases: malformed model outputs, partial responses under token limits, format drift across model versions, and graceful degradation when the model cannot produce a valid structured response. Integration work includes webhook handlers, HL7/FHIR data transformation for clinical system compatibility, and bi-directional sync with EHR platforms for applications that both read from and write to patient records.