AI Agent Development Company in San Francisco

The number follows what you actually build: a focused agent proof of concept at SF rates, a production agent for a mid-market SF buyer (customer service deflection, internal IT or HR helpdesk, claims processing, legal research, engineering workflow assistant), or enterprise programs with multi-agent orchestration, SWE-bench Verified evaluation, HIPAA BAA. Scope drivers include tool integration, evaluation harnesses in CI, audit logging, prompt injection red-team, and integration with two or three source systems. SF rates run above other US markets because of the OpenAI, Anthropic, Sierra, and Cognition Labs talent premium, but we deliver from Edmonton and Chandigarh on PST hours so headline rates compress meaningfully. Fixed-fee proposals in USD, with one-time build cost separated from ongoing inference, tool execution, and reranking fees.

Claude Computer Use lets Claude Sonnet 4 and Opus 4 read screenshots, move a mouse, type into fields, and operate a desktop or browser end-to-end. OpenAI Operator drives a browser autonomously through the Assistants API surface. Both are powerful and both amplify the attack surface, so production deployments need real engineering discipline. Our deployments sandbox the browser or desktop environment in an ephemeral VM on Modal or AWS Fargate us-west-2 with no access to customer credentials beyond a scoped session token, retrieve credentials at action time through a secret broker that records every grant, classify supply-chain prompt injection on every page the agent visits before that content enters the model context, gate state-changing actions (purchases, account changes, sends) behind human-in-the-loop confirmation, and enforce per-session monetary and rate caps at the tool layer regardless of model instructions. Every plan, screenshot, tool call, and observation is logged to a tamper-evident store the customer security team can sample, and the kill-switch is one click away.

Customer-service agents at SF B2B SaaS and consumer scale need grounded responses, strict tool allow-lists, refund and credit caps enforced in the tool layer rather than the system prompt, a human handoff protocol with full conversation context, prompt injection defenses, and an audit trail that satisfies CCPA and CPRA on consumer interactions plus the customer's internal compliance team. We build these on Anthropic Claude Sonnet 4 or OpenAI GPT-4o through the Assistants API depending on residency and contractual posture, with retrieval against the customer's product, plan, billing, and policy corpora through Cohere or Voyage Rerank. Tool actions that can spend money, change a subscription, release a credit, or modify account settings are gated by a confirmation step and a per-session monetary cap that the model literally cannot exceed. Escalation behavior, refusal on out-of-policy prompts, and tone are encoded as test cases in the evaluation harness that runs in GitHub Actions on every change. Sierra-tier deployments also run continuous shadow-mode evaluation against a human-agent baseline.

Devin-pattern agents need a sandboxed environment that can open a shell, edit code, run tests, and push pull requests without compromising the customer's source control or production systems. Our deployments run agent execution in ephemeral E2B or Modal sandboxes with the customer's repository checked out under a scoped GitHub App token, evaluate on SWE-bench Verified plus a client-specific repository-aware gold set, gate every pull request behind required human review with branch protection, and refuse any action that touches production infrastructure or secrets without explicit human approval. We log every shell command, file edit, test run, and tool call to a tamper-evident store, integrate with the customer's existing SIEM, and produce a SOC 2 control mapping for the audit. For high-trust deployments we run continuous evaluation in production against a held-out task set so a model or prompt regression fails the next deploy. Cursor Enterprise and Copilot Workspace remain the primary IDE-native surfaces; full Devin-pattern agents fit where the workflow is repetitive enough to justify autonomy.

Agents amplify prompt injection risk because a successful injection can convince the model to call a tool with state-changing authority. Our defenses are layered. Every tool is explicitly allow-listed and scoped to least privilege at the IAM and API key level. Every state-changing tool call is gated by a human-in-the-loop confirmation in production. Retrieval sources are allow-listed and content-sanitized, with a classifier pass on every retrieved document and every web page or email the agent reads before that content enters context. Output schemas are validated through structured generation. Supply-chain prompts (web pages, emails, attachments, screenshots in Computer Use) are sandboxed and treated as untrusted. Per-session monetary and impact caps apply at the tool layer regardless of model instructions. We red-team every agent against direct, indirect, and supply-chain payloads aligned with the OWASP LLM Top 10 and MITRE ATLAS, plus a client-specific payload set, and ship a written report with severity ratings, fix recommendations, and a retest pass before launch.

We pick by problem shape, not vendor preference. LangGraph is our default for explicit stateful graphs where the workflow has named nodes, conditional routing, and durable state, which fits customer service, claims processing, and any agent that needs to survive across long sessions or retries because the graph itself becomes the auditable artifact. AutoGen, which carries Microsoft Research SF and Redmond contributions, fits multi-agent conversation patterns where role-based agents (planner, executor, critic) iterate toward a solution, typically engineering and analysis workloads. CrewAI fits lighter role-based deployments where the team prefers a simpler abstraction, and it is broadly adopted across YC W24 and W25 batches we work with. Letta fits when long-running memory across sessions is a first-class requirement, particularly for personal assistant and account-manager surfaces. For evaluation we run AgentBench, GAIA, SWE-bench Verified for code-modifying agents, HumanEval, ToolBench, and τ-bench for tool-use agents, plus a client-specific gold set. The chosen framework, the rationale, and the evaluation results are documented in the model card.

CCPA and CPRA give California consumers the right to access, delete, and opt out of automated decision-making that profiles them, and the California Privacy Protection Agency is actively rulemaking on automated decision-making technology. For consumer-facing agents we wire the access, deletion, and opt-out endpoints required by CPRA into the agent surface, tag every decision log with the consumer identifier required to honor a deletion request, and apply data minimization at ingestion so the log surface is no larger than the workflow requires. Decision logs are tenant-scoped, queryable by the customer privacy officer, and retained on a documented schedule aligned with the underlying processing purpose. For high-impact agent decisions (employment, credit, healthcare access, or material consumer impact) we add a human-in-the-loop gate and a written explanation surface, anticipating both CPPA rulemaking and the Colorado AI Act on high-risk decision systems.

A SF production agent build typically takes twelve to twenty-two weeks from kickoff to launch, depending on tool inventory and regulatory tier. Weeks one to three are discovery, scope-of-authority workshop, AB 2013 review on the underlying model, CCPA and CPRA review on agent decision logging, SOC 2 control mapping, OWASP LLM Top 10 and MITRE ATLAS threat model, and tool-inventory lock. Weeks four to eight are agent design, retrieval and tool integration, evaluation set construction on AgentBench, τ-bench, or SWE-bench Verified plus a client-specific gold set, and the first runnable end-to-end loop. Weeks nine to fourteen are tool hardening, human-in-the-loop gate design, audit logging, prompt injection red-team testing, and integration with two or three source systems (Salesforce, Zendesk, Stripe, Notion, GitHub, internal APIs). Weeks fifteen to eighteen are shadow mode, security review, SIEM and observability wiring, and a tabletop exercise on the kill-switch. Week nineteen onward is gradual rollout with measured success rate, escalation rate, and USD cost per resolved task. Programs with HIPAA scope can extend to twenty-eight weeks.