AI Agent Development Company in Singapore

The number follows what you actually build: a scoped AI agent proof of concept at Singapore rates, a production AI agent system for a mid-market Singapore enterprise (a MAS Veritas-aligned banking agent with Singpass federation, a SEA-LION-backed ASEAN dispute-resolution agent, or a Government Commercial Cloud citizen-services agent), or enterprise multi-agent programmes with supervisor and worker agents, multi-model routing across SEA-LION, Apollo, Claude on Bedrock ap-southeast-1, and Azure OpenAI Southeast Asia, full Singpass biometric step-up. Scope drivers include agent orchestration, identity federation, action-permission matrix, observability, Project Moonshot baselines, AI Verify reporting, and integration with two to four backend systems. Singapore rates sit above Kuala Lumpur, Jakarta, and Bangkok because of the MAS-regulated and IM8-cleared talent premium, but below Sydney and Tokyo at equivalent quality. Eligible portions may qualify for Enterprise Singapore EDG, IMDA grants, EDB incentives, or AI Singapore 100 Experiments co-funding. We give fixed-fee proposals in SGD.

MAS Notice 656 sets the cyber requirements that apply to AI use by financial institutions in Singapore, complementing MAS Notice 655 on broader cyber hygiene and the MAS Technology Risk Management Guidelines. For AI agents specifically, Notice 656 expectations include controlled access to AI systems, evaluation before production deployment, monitoring of agent behaviour, incident reporting, and clear accountability for AI-driven actions. Our Singapore agent designs incorporate Notice 656 controls from sprint one: identity-bound access to the agent through Singpass or Corppass, role-based authorisation for each agent action, an action-permission matrix that constrains what the agent can do regardless of how the user phrases a request, mandatory Project Moonshot evaluation baselines and MAS Veritas fairness assessments before any customer-affecting agent reaches production, real-time monitoring of agent behaviour piped to the bank's SIEM, and incident-response runbooks aligned with MAS Notice 656 and the PDPA 72-hour breach notification regime. For transactional agents, MAS Notice 626 AML/CFT requirements layer on top, and Singpass biometric step-up gates high-value or high-risk actions.

Transactional agents that initiate actions on behalf of an individual (transferring money, changing account details, filing a tax return, submitting a work-permit application, updating CPF nomination) federate to Singpass through the National Digital Identity (NDI) APIs, with Singpass Face Verification or Singpass Mobile biometric authentication required as a step-up for high-value or high-risk actions. The threshold for step-up is set in discovery against the client's risk appetite and MAS, IM8, or agency policy: a low-risk informational action does not require step-up, a medium-risk action (a balance enquiry, a non-binding KYC update) requires standard Singpass authentication, and a high-risk action (a fund transfer above a threshold, a binding submission to IRAS or MOM, a change to a CPF nomination) requires biometric step-up. The agent itself does not handle credentials; it requests step-up through the NDI API, the user authenticates on their own Singpass Mobile app, and a signed assertion returns to the agent authorising the specific action. This pattern keeps Singpass terms of use respected, satisfies MAS Notice 656 and IM8 expectations on identity-bound action authorisation, and produces a clean audit trail.

Project Moonshot is IMDA's open-source LLM safety and evaluation framework, developed with the AI Verify Foundation. For LLM-backed agents, Project Moonshot covers the reasoning surface (hallucination, toxicity, bias, jailbreak resistance, prompt injection robustness, capability benchmarks across English and ASEAN languages), but agent evaluation extends beyond that. We layer custom agent benchmarks on top of Project Moonshot covering goal completion (does the agent finish the task correctly?), off-policy actions (does the agent ever take an action outside its permission matrix?), harm (does the agent ever produce a harmful action or output?), latency and cost per goal-completion, and tool-use hijack robustness (can a crafted retrieval or user input cause the agent to call a tool it should not?). The combined Project Moonshot plus agent evaluation pack regenerates in CI on every material release of prompts, reasoning model, or tool surface, and the report is included in the AI Verify governance pack we ship to MAS, IMDA, SNDGO, CSA, or HSA reviewers. For ASEAN multilingual agents the suite extends to SEA-LION-aligned benchmarks in Bahasa Indonesia, Thai, Vietnamese, Tagalog, Tamil, and Singlish.

Yes. Singapore Government Commercial Cloud (GCC) is the abstraction layer that GovTech maintains over AWS ap-southeast-1, Azure Southeast Asia, and GCP asia-southeast1 for Whole-of-Government workloads, with Instruction Manual 8 (IM8) and additional security requirements layered on top. For agents in government, we typically self-host SEA-LION 70B or AISG Apollo on GCC GPU capacity for any agent that touches citizen data, OFFICIAL CLOSED, or higher classifications. Anthropic Claude on Bedrock ap-southeast-1 and Azure OpenAI Southeast Asia are usable for OFFICIAL OPEN content and internal productivity agents subject to the agency's data-loss-prevention controls. Every GCC agent deployment ships with an AI Verify report, a Project Moonshot evaluation pack, an action-permission matrix mapped to the agency's policy domain (IRAS tax workflows, MOM work-permit workflows, HDB property workflows, CPF nomination workflows), Singpass or Corppass identity federation for citizen-affecting actions, IM8-aligned security posture, and audit logging that GovTech and SNDGO architecture reviewers accept. We have patterned deployments after public references such as Pair, AskJamie's evolution, LawNet AI, and lawsg-LLM.

Tool-use hijack is the agent-specific cousin of prompt injection: a crafted retrieval result, user input, or upstream system response causes the agent to call a tool, change a parameter, or take an action it should not. MAS Notice 656, IM8 security reviewers, and CSA increasingly require evidence that this has been tested. Our standard Singapore agent defence is layered: a hard action-permission matrix enforced outside the LLM, so even if the model is convinced to act, the runtime refuses; instruction-layer separation between system prompt, tool description, retrieval context, and user input; tool descriptions that explicitly state which inputs are untrusted; output validation against schemas before any tool call executes; benchmark coverage in CI for prompt-injection and tool-use hijack (PromptInject, HouYi-style suites, and custom Singapore-specific suites); identity-bound step-up via Singpass or Corppass for high-risk actions, so an injected agent cannot escalate without the user's explicit biometric or device authorisation; audit logging of every tool call piped to the SIEM; and a permission-budget model where the agent has a fixed budget of state-changing actions per session beyond which it must escalate to a human. For Whole-of-Government and MAS-regulated deployments we deliver the test results as part of the AI Verify governance pack.

A typical Singapore AI agent build (MAS Veritas-aligned banking agent with Singpass federation, GCC citizen-services agent under IM8, SEA-LION-backed ASEAN dispute-resolution agent, or healthcare-cluster clinical-documentation agent) takes sixteen to twenty-six weeks from kickoff to production, assuming clean source content and MAS TRMG, IM8, or HSA scoping as part of scope. Week 1 to 4 is agent-design workshop, MAS Notice 656 or IM8 review, AI Verify principle mapping, Project Moonshot scoping, action-permission matrix design, and human-oversight model. Week 5 to 12 is agent orchestration, tool surface implementation, identity federation (Singpass, MyInfo, Corppass), prompt-injection and tool-use hijack defences, evaluation harness construction, and Project Moonshot plus agent-evaluation baselines. Week 13 to 18 is MLOps, observability, shadow-mode deployment against production traffic, internal model risk and IT risk review, and MAS outsourcing notification where applicable. Week 19 to 22 is AI Verify report, MAS Veritas fairness assessment where applicable, IM8 security review for GCC, and CSA review for cybersecurity-adjacent agents. Week 23 onward is gradual rollout with continuous Project Moonshot and agent-evaluation regression. We have shipped to this cadence on ap-southeast-1, Azure Southeast Asia, and Government Commercial Cloud.

Multi-agent orchestrations make sense when a workflow has natural sub-tasks with different tool surfaces, different evaluation criteria, or different risk classifications. We typically design a supervisor agent that decomposes the request, routes sub-tasks to worker agents, and aggregates results, with worker agents holding narrower tool surfaces and narrower permission matrices. For institutional-banking onboarding, a supervisor agent might orchestrate a KYC worker, an AML worker, a credit worker, and a documentation worker, each with its own MAS Veritas fairness assessment, Project Moonshot baseline, and AI Verify report scoped to its task. For ASEAN logistics dispute resolution, a supervisor agent might orchestrate a SEA-LION-backed customer-comms worker (handling the user in Bahasa, Thai, or Vietnamese), a Singpass-federated identity worker, an evidence-gathering worker, and a resolution-execution worker. The supervisor never holds tool permissions itself; it only holds the routing permission. Each worker's actions are audited separately, and the supervisor's decisions are audited as a routing log. This pattern keeps the action-permission matrix tractable as the system scales and gives MAS examiners, IMDA reviewers, or SNDGO architecture reviewers a clean way to assess each agent independently.