AI Agent Development Company in Seattle

The number follows what you actually build: a scoped agent proof of concept at Seattle rates, a production agent system with full Copilot or Bedrock integration, multi-tool reliability, evaluation harness gated in CI, and Marketplace or AppSource submission readiness, or full enterprise agent platforms with partner-team review, SOC 2 Type II controls, Washington My Health My Data Act compliance where applicable, and multi-cloud deployment. Scope drivers include tool design, a baseline agent, an evaluation harness, and a hosted demo on Copilot Studio, Bedrock Agents, or Agentforce. Seattle rates sit above Portland and Denver because of the Microsoft, Amazon, and Tableau talent benchmark and the partner-team submission premium. Fixed-fee proposals, not open T and M.

Yes. We ship Microsoft 365 Copilot declarative agents (built with Copilot Studio agent builder), custom engine agents (built with Teams Toolkit and the Bot Framework SDK), and AI Foundry agents for the broader Azure surface. Architectures use Semantic Kernel for orchestration, Microsoft Graph for context (mail, calendar, files, Teams messages, SharePoint), and Azure OpenAI under enterprise data protection commitments. We deliver to the AppSource and Microsoft 365 Certification review standards from day one, including data handling documentation, manifest validation, and accessibility compliance. For Teams-channel agents we wire in the Bot Framework state management and adaptive cards that Microsoft partner reviewers actually expect, and our submissions typically clear initial review on the first or second attempt.

Yes. We ship Bedrock Agents with action groups (Lambda-backed tool calls), knowledge bases (managed retrieval over S3-backed corpora with OpenSearch Serverless or Pinecone), guardrails (denied topics, content filtering, PII redaction), and prompt orchestration patterns that match AWS engineering guidance. For multi-agent collaboration we use the Bedrock Agents supervisor pattern. For AWS Marketplace ISVs we deliver to the AMI and SaaS listing review standards including IAM least privilege, CloudTrail audit logging, and KMS-managed encryption. Model selection typically spans Anthropic Claude (Sonnet and Opus on Bedrock), Amazon Nova, and Meta Llama 3 depending on cost and latency requirements. We have patterned deployments after public AWS reference architectures from re:Invent and the AWS Generative AI Innovation Center.

The Washington My Health My Data Act (RCW 19.373), in effect since 2024, regulates consumer health data far more broadly than HIPAA. It applies to any business that processes consumer health data of Washington residents, even if not a HIPAA covered entity, and defines consumer health data to include data relating to health condition, treatment, biometrics, gender-affirming care, reproductive and sexual health, precise location near a health facility, and inferences derived from any of those. For agents that touch wellness, fitness, mental health, period or fertility tracking, telemedicine adjacencies, or even retail purchases that imply health condition, we deliver consent capture aligned with the Act, a Consumer Health Data Privacy Policy, data subject rights endpoints (access, deletion, withdrawal of consent), and a strict separation of consumer health data from other processing. Civil enforcement is by the Washington AG plus a private right of action, so violations carry real teeth, not just speculative risk.

Yes. Tableau (Salesforce) ships Einstein Copilot for analytics from Fremont and Salesforce drives heavy Agentforce adoption across enterprise. We build Agentforce topics, actions, and custom prompts that integrate with Sales Cloud, Service Cloud, and Data Cloud, ship Lightning Web Components that surface agent interactions, and respect the Salesforce platform limits on prompt and context size. For ISV listings on AppExchange we deliver to the Salesforce security review standards including XSS prevention, CRUD/FLS enforcement, and proper handling of Org credentials. For Tableau-adjacent analytics agents we integrate with Tableau Pulse and the VizQL Data Service so the agent can query and explain dashboards in natural language.

Seattle clients often run on both Azure and AWS simultaneously, and our agent platforms reflect that. We default to a cloud-neutral orchestration layer (LangGraph, Semantic Kernel, or AutoGen) running in containers on Azure Container Apps or Amazon ECS, with the LLM backend abstracted behind a router that selects Azure OpenAI, AWS Bedrock, or Anthropic direct API based on latency, cost, and data residency rules. Observability federates LangSmith or LangFuse on top of native Azure Application Insights and AWS CloudWatch traces. For state we use Azure Cosmos DB or Amazon DynamoDB depending on the primary cloud, with cross-region replication for disaster recovery. This pattern is common at Seattle ISVs who sell into customers on both clouds and refuse to be single-cloud-locked.

A typical Seattle B2B SaaS agent (product copilot, sales coaching, content discovery, infrastructure operations) takes fourteen to twenty-two weeks from kickoff to production, assuming clean APIs and existing knowledge base content. Week 1 to 4 is discovery, partner-platform selection (Copilot vs Bedrock vs Agentforce vs custom), tool design, and an evaluation harness baseline. Week 5 to 12 is build, action group implementation, red-teaming for prompt injection through user inputs and tool outputs, and shadow mode against the human baseline. Week 13 to 18 is gradual rollout, partner-platform submission (AppSource, AWS Marketplace, AppExchange), and security review remediation. Week 19 onward is scale. We have shipped to this cadence inside Microsoft and AWS partner ecosystems.

Every production agent ships with a CI-gated evaluation harness, not just launch metrics. We standardise on Promptfoo and Braintrust for prompt-level evaluation, LangSmith and LangFuse for trace-level inspection of multi-step agent runs, Azure AI evaluation SDK for Copilot deployments, and Bedrock Agents native traces for AWS deployments. Custom harnesses score task completion, tool-call accuracy, hallucination rate, escalation triggers, and latency budgets. For Microsoft 365 Copilot submissions we add the responsible AI checks and content moderation tests that AppSource reviewers expect, and for Bedrock we add Bedrock Guardrails policy tests as a release gate. Every build fails if task completion drops more than a configurable threshold against the regression set.