AI Agent Development Company in Tokyo

Agent programmes here are priced by the evidence burden they carry: a scoped AI agent proof of concept at Tokyo rates, a production single-purpose agent (customer service, document intake, internal helpdesk), or multi-agent systems with banking-grade FISC documentation, sovereign deployment, and parallel-run validation. Scope drivers include use-case scoping, a Japanese-language eval set, a baseline agent with one or two tools, and a hosted demo. Tokyo rates sit above Osaka and Fukuoka because of FISC and AISI coordination overhead plus native Japanese linguistic review. We quote fixed-fee against scope, not open T and M.

The AI Guidelines for Business (AI事業者ガイドライン), published by METI and MIC, are Japan’s primary soft-law framework for AI development and deployment. They define principles around human-centric design, safety, fairness, privacy, security, transparency, accountability, education, fair competition, and innovation. For agent development this translates into explicit risk classification, documented refusal behaviour, audit logging of every tool call and every model response, transparency disclosures when users are interacting with an agent rather than a human, and accountability ownership defined inside the client organisation. Our discovery phase produces a guideline-aligned risk and mitigation memo in Japanese for ringi-sho approval, and our agents ship with the disclosure and logging hooks the guidelines anticipate.

The Japan AI Safety Institute (AISI) was established in 2024 under IPA (Information-technology Promotion Agency) to coordinate AI safety research, evaluation methodology, and information sharing with international counterparts including the US AISI and UK AISI. For high-impact agent deployments in regulated sectors, we map the use case against AISI-published evaluation expectations, document model evaluations on red-team scenarios derived from AISI guidance, and prepare the disclosure package for cases where the client chooses to share results with AISI. We do not act as a regulatory intermediary, but we package the artefacts so the client’s appointed AI governance officer or external counsel can engage AISI directly when the use case warrants.

Yes. Customer-facing agent responses ship in keigo by default, with sonkeigo for actions the user performs and kenjougo for actions the system performs, reviewed sprint by sprint by a native Japanese linguist before deployment. Refund, return, account inquiry, identity verification, and dispute flows use the conversational patterns established by Rakuten, Mercari, and LINE customer support, with handover to a human operator when confidence falls below a calibrated threshold or when emotional escalation signals are detected. We also handle the Japanese-specific edge cases (kanji name verification, full-width and half-width input, address kanji disambiguation, dates in Japanese era format Reiwa where the client uses it). Eval sets include native-reviewed keigo correctness scoring alongside factual accuracy.

Banking agents at MUFG-, SMBC-, and Mizuho-tier clients run under FISC Security Guidelines 11th edition operational controls and J-FSA supervisory guidelines on outsourcing. For agents this means: data residency in ap-northeast-1 (Tokyo) or Japan East with no cross-border inference unless an explicit FISC-aligned outsourcing risk assessment signs off, full audit trail of every tool call and every model response retained for the supervisor’s required retention period, human-in-the-loop gates on any action with monetary or customer-record consequence, segregation of duties between the agent’s tool permissions and the human reviewer’s approval permissions, and a documented rollback plan. Sovereign open-weight model deployment (Llama 3, Qwen) on GPUs inside the bank’s VPC is common where frontier APIs are not approved for the workload.

Our discovery phase produces an APPI data flow map covering acquisition, retention, use, third-party provision, and cross-border transfer of personal information, plus a notice of utilisation purposes (利用目的) suitable for in-product display. Personal information stays in Japanese regions by default. The agent’s tool registry explicitly tags which tools touch PII, which require additional consent, and which write to systems of record. Audit logs retain every PII read and write for the APPI retention window. Breach notification hooks fire to the appointed PPC liaison within the APPI reporting threshold. We do not allow agent responses to echo PII back to the user unless the use case explicitly requires it, and we run synthetic-data evals to confirm the agent does not leak training data.

Yes, where the use case warrants it. Sakana AI’s public research on evolutionary model merging and agent fusion suggests that combining specialist models often outperforms a single large generalist on domain tasks, particularly in Japanese-language workloads where a Japanese-tuned smaller model can outperform a frontier model on certain narrow tasks. For Tokyo clients we have shipped multi-agent architectures with specialist agents for retrieval, planning, code execution, and Japanese-language linguistic review, coordinated through LangGraph or a custom orchestrator. We do not oversell evolutionary fusion as a universal answer, and for many use cases a single well-prompted frontier model with good tool design beats a complex multi-agent system. Discovery decides which approach actually fits your problem.

Yes. For MUFG-, SMBC-, Mizuho-tier banking clients and for pharmaceutical and government-linked clients where APPI and sector-specific rules prohibit cross-border inference, we deploy open-weight models (Llama 3 series, Qwen 2.5, Mistral, Sakana-published evolved models where licensing permits) on GPU clusters inside the client’s VPC or on-prem datacentre. Inference stacks run vLLM, TGI, or NVIDIA NIM. Embedding stores run pgvector on PostgreSQL or Milvus on the client’s storage. Orchestration runs LangGraph or a custom Java or Python orchestrator depending on the client’s platform. Observability and audit logs flow to the client’s existing SIEM (Splunk, QRadar) under FISC-aligned retention. We do not require any data to leave the client environment.