12 Best Agentic AI Companies in Singapore (2026)

What is an agentic AI company? {#what-is-an-agentic-ai-company}

An agentic AI company designs, builds or supplies AI agents: software that receives a goal, plans the steps, calls tools and business systems to carry those steps out, checks the result and continues until the task is done or a person intervenes. The defining difference from a chatbot is that an agent acts. A chatbot answers a question about a refund; an agent issues the refund.

That single difference changes what a buyer evaluates. A wrong answer from a chatbot is an embarrassment that a person can correct. A wrong action from an agent is a transaction, a record change or a message that has already happened. The engineering work in an agentic AI engagement therefore splits into two parts:

1. The capability layer. The model, the prompts, the tools the agent can call, the memory it keeps and the orchestration that decides which agent does what.
2. The control layer. The permissions that decide which actions each agent is allowed to take, the checkpoints where a person must approve, the limits on value and volume, the logs that record every step, and the mechanisms that reverse or contain a bad action.

Demonstrations show the first layer. This guide weights the second layer more heavily, because a wrong action taken without the right permission, approval or limit has already happened by the time anyone reviews it.

Agentic AI companies serving Singapore fall into three broad delivery models, and the reviews later in this guide include all three:

• Global integrators with a Singapore practice. Large consulting and technology firms that assemble agents from several vendor platforms, add governance advisory and run multi-country programmes.
• Local AI builders. Singapore-headquartered firms that build custom agents or run a focused agent product, often in one channel such as voice or messaging.
• Agent platform vendors with Singapore delivery. Software companies whose platforms host and govern agents, sold and supported through a Singapore office and a partner network.

Typical first agents in Singapore enterprises share three traits: narrow scope, high volume and output that a person can check. Examples include IT and customer support triage (classify the ticket, gather account context, draft the reply, and stop for approval before changing a customer record), document intake and routing, finance exception queues, marketing budget adjustments within set limits, and scheduled research or monitoring runs that end in an alert rather than an action. Each of these can start with a small permission set and grow as the agent builds a logged track record. Workflows that are poor first candidates are those where nobody agrees what a correct outcome looks like, where an error is irreversible and no accountable person owns the approval, or where a fixed script already does the job.

A practical working definition for procurement is this: an agentic AI company is the partner who will decide, with you, what your agents are allowed to do when nobody is watching. Framed that way, the first shortlisting question stops being “how capable are your agents?” and becomes “show me how your agents are stopped.”

Why action controls matter more than autonomy in 2026 {#why-action-controls-matter-more-than-autonomy}

• Agents are spreading inside existing software. In a Gartner press release of 26 August 2025, Gartner predicted that 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% in 2025.
• Large organisations are scaling them. McKinsey’s “The state of AI in 2026: On the road to ROI” (25 August 2026) found that 40% of respondents from organisations with revenues above US$1 billion report scaling AI agents, up from 27% a year earlier, while the share for smaller organisations stayed at 22%. The survey drew 1,719 responses.
• Cost, value and controls are the forecast cancellation risks. In a press release of 25 June 2025, Gartner predicted that over 40% of agentic AI projects will be cancelled by the end of 2027 because of escalating costs, unclear business value or inadequate risk controls.
• Many “agentic” offers are relabelled. The same Gartner release estimated that only about 130 of the thousands of vendors claiming agentic capability are real, with many rebranding assistants, robotic process automation and chatbots as agents.
• High performers manage unintended actions. The McKinsey survey reports that its AI high performers are much more likely than others to work on mitigating unauthorised or unintended actions by AI systems.

The engineering guidance points the same way. Anthropic’s Building effective agents (19 December 2024) notes that the autonomy of agents brings higher costs and the potential for compounding errors, and recommends extensive testing in sandboxed environments with appropriate guardrails. It also recommends finding the simplest solution possible and adding complexity only when it is needed.

Put together, these sources describe a market in which autonomy is easy to demonstrate and control is hard to build. Hiring an agentic AI firm on demo autonomy works like appointing a treasury operator on typing speed: the control that matters is who approves the payment. A demo that shows an agent completing a long chain of steps tells you about the capability layer. It tells you nothing about what happens when the agent is wrong at step four, whether anyone notices, and whether the change can be undone.

Three failure patterns that action controls prevent

Three practical failure patterns show why these controls matter. They are examples rather than a measured ranking of causes, and each has a matching control.

The runaway loop. An agent retries a failing step, re-plans and retries again, calling a large model each time. Nothing is broken in the usual sense, yet cost climbs and the task does not complete. The control is a step cap and a spend limit per run, with the run stopping and alerting when either is reached.

The stale approval. An agent proposes a change, the approver sees it hours later and approves it, but the conditions that justified it have moved. In advertising, pricing or inventory, a correct recommendation at 9am can be a costly one at 3pm. The control is a proposal that expires, so approval refers to current conditions, and a hard cap on how large any single change can be.

Privilege creep. An agent is given broad access during a pilot “to save time”, and the access is not narrowed afterwards. Months later the agent can write to systems its task did not need. The control is a written permission register reviewed like code, with a separate identity per agent so every action can be traced.

A partner that has run agents in production will recognise all three patterns and explain how its designs contain them. A partner that has only run demonstrations will tend to talk about model choice instead.

What this means for your shortlist

For a Singapore enterprise, this changes the shortlist in three ways. First, the partner’s evidence of action controls in production outweighs the length of its demo. Second, look for a partner that can explain which actions it refuses to automate for your use case. Third, the artefacts that prove control (permission maps, approval records, run logs and rollback procedures) need to be deliverables that you keep, not features that stay inside the vendor.

What to look for in an agentic AI company {#what-to-look-for-in-an-agentic-ai-company}

1. A written action permission register

Every agent needs to run under its own identity with the narrowest access its task needs, recorded in a register that lists each tool, each system and each action type the agent is allowed to use. Ask the partner to show a permission register from a previous build (redacted if necessary). A partner that runs agents under a shared service account or a staff member’s login leaves your systems of record unable to attribute an action to a specific agent; attribution then depends entirely on the partner adding reliable agent identifiers and structured logs at the application level, so ask to see them.

2. Approve-before-execute checkpoints on irreversible actions

Actions that move money, change a live customer-facing asset, contact a customer or cannot be reversed need to stop for a named human approver, with the approval recorded. Strong designs also make the proposal expire, so a stale recommendation cannot be approved hours later against changed conditions. As one example from our own delivery, Synthesised.ai’s Perispark build for a management consultancy expires any AI-proposed action that touches a live budget or live site after 15 minutes if nobody approves it, and every AI-executed action is audit-logged (the expiry is set in the build’s code; Synthesised.ai company records). Ask each shortlisted partner which action classes in your use case stop for approval, and who approves them.

3. Hard limits on value, volume and scope

Approval alone is not enough when an approver is busy. Good agent designs add hard limits that no approval can exceed without a change request: a maximum budget change per action, a cap on the number of records touched per run, a ceiling on refunds or discounts, and a list of systems the agent is barred from writing to. Ask where these limits live in code and who can change them.

4. Complete, reconstructable run records

Every run needs to leave a trace of the inputs, the plan, each tool call, each output, the approver (where one was required) and the cost, enough to reconstruct what happened. Exact replay is a separate, stronger requirement: models can return different outputs to the same input and external systems change, so if you need replay, specify the recorded model version, parameters, tool responses and state snapshots. The test is simple: pick any run from last week and ask the partner to reconstruct what the agent did and why. If the answer relies on reading model output rather than structured logs, the logging is not production grade.

5. Rollback and containment

A production agent needs a documented way to reverse or contain a bad action: versioned changes that can be restored, a staging step for high-risk writes, and a way to pause one agent or all agents immediately. Ask how a wrong record change, a wrong message or a wrong budget change is undone, and how long it takes.

6. Evaluation before every release

A change to a prompt, model version or tool can alter an agent’s behaviour without any code change a reviewer notices. Ask the partner to test every agent against a fixed evaluation set before each release, including cases where the correct behaviour is to refuse or escalate. Ask to see the evaluation set’s structure and the pass threshold agreed with the client.

7. Cost limits per run and per agent

An agent that loops, retries or calls a large model for a small task spends money without finishing work. Look for step caps, token budgets and per-agent spend limits, with cost recorded per run. This is one of the three cancellation causes in the Gartner prediction above, so it belongs in the first conversation, not the last.

8. Artefacts you keep at handover

The permission register, checkpoint policy, evaluation set, run logs and rollback procedures need to be handed over as documents and code that your team can operate. A partner whose agents only it can explain has built a dependency, not a capability. Ask for a sample handover pack from an earlier engagement, and check that it lets a new engineer answer three questions without calling the vendor: what each agent is allowed to do, who approves what, and how a bad action is reversed. Open connection standards help here too: agents that reach tools through a documented protocol can reduce integration friction if you later change platform or partner, although authentication, permissions, state and vendor-specific tooling still need to be tested in any migration.

9. Singapore data handling and residency

Agents that read or write customer or employee records handle personal data, so the design needs to take account of Singapore’s Personal Data Protection Act (PDPA), which, in the PDPC’s words, provides a baseline standard of protection for personal data in Singapore. Ask where model calls are processed, where logs are stored and for how long, and whether a Singapore-region deployment is available for your stack. Run logs deserve particular care: a complete log of an agent’s actions often contains the very personal data the agent handled, so agree its retention and access rules as carefully as the agent’s own permissions.

How agentic AI delivery models compare {#how-delivery-models-compare}

The three delivery models place the control layer in different hands. The table below summarises the trade-offs a buyer can expect, before any individual company is considered.

Governing agents in Singapore: IMDA, PDPA and CSA guidance {#governing-agents-in-singapore}

IMDA’s Model AI Governance Framework for Agentic AI. First launched by IMDA on 22 January 2026, the framework is now at Version 1.5, published on 20 May 2026 and updated on 5 June 2026. IMDA’s factsheet on the update says it adds best practices and real-world case studies, including on multi-agent systems, third-party agents and automation bias, and treats agent identity as a core measure for bounding risk. As of September 2026, the current version remains voluntary guidance organised around four dimensions:

1. Assessing and bounding the risks upfront by selecting appropriate agentic use cases and placing limits on agents’ powers, such as their autonomy and access to tools and data.
2. Making humans meaningfully accountable for agents by defining significant checkpoints at which human approval is required.
3. Implementing technical controls and processes throughout the agent lifecycle, such as baseline testing and controlling access to whitelisted services.
4. Enabling end-user responsibility through transparency and education or training.

The first three dimensions map directly onto the criteria above: the permission register, per-agent identity and hard limits (dimension 1), approve-before-execute checkpoints (dimension 2), and evaluation, logging and rollback (dimension 3). A partner who cannot map its design to these dimensions is unlikely to produce the evidence a Singapore procurement or risk review will ask for.

The PDPA. Where your organisation deploys an agent for its own purposes, the agent’s collection, use and disclosure of personal data are generally attributed to your organisation, so the PDPA’s obligations on purpose, consent or another basis the Act allows, protection and retention apply to what the agent does, subject to the Act’s scope and exceptions. A vendor is not automatically a data intermediary. Under the PDPC’s Advisory Guidelines on Key Concepts, a data intermediary processing personal data on your behalf under a written contract carries the protection, retention limitation and breach notification obligations, while a provider that uses the data for its own purposes can carry the full set of obligations itself. Agree each party’s role in the contract, and check it against how the data actually flows.

CSA’s agentic AI security addendum. The Cyber Security Agency published its addendum on securing agentic AI systems on 17 June 2026, after a public consultation that ran from 22 October to 31 December 2025, as a companion to its Guidelines and Companion Guide on Securing AI Systems. It covers mapping agentic workflows to find where threat actors can exploit them, and practical controls across the development lifecycle.

OWASP’s agentic risk list. The OWASP Top 10 for Agentic Applications, published in December 2025, names risks including agent behaviour hijacking, tool misuse and exploitation, and identity and privilege abuse. Each of these is narrowed by per-agent identity, least-privilege permissions and complete run logs.

How to shortlist an agentic AI company {#how-to-shortlist-an-agentic-ai-company}

Step 1: List the actions, not the use case. Write down every action the agent will take in live systems (for example: read the ticket, update the CRM field, issue a refund under a set value, email the customer). Mark each action as reversible or irreversible, internal or external, and low or high value. This list is the brief.

Step 2: Decide your approval policy before you meet vendors. For each high-value or irreversible action, name the approver role and the maximum the agent is allowed to do without approval. Vendors then design to your policy rather than selling you theirs.

Step 3: Ask for the “refusal demo”. Instead of asking a shortlisted company to show what its agent can do, ask it to show an action the agent was not allowed to take: the permission check blocking it, the approval request it raised, the log entry it left and how the approver saw it. A partner that cannot run this demo has not built the control layer.

Step 4: Check the evidence type. Separate evidence into three kinds: third-party evidence (named client references, independent reviews, regulator or directory listings), vendor documentation of control features, and vendor-reported outcomes. All three are useful; they are not equally strong. Where outcomes are client-reported, say so in your evaluation notes.

Step 5: Agree the handover artefacts in the contract. Name the permission register, checkpoint policy, evaluation set, run logs and rollback procedures as deliverables, with the format and the retention period.

The evidence types are worth separating explicitly, because vendor pages mix them freely.

Three questions separate strong partners quickly:

• “Which of our actions do you refuse to automate, and why?”
• “Show me the log of a run that went wrong in production and what you changed afterwards.”
• “If we end the contract, what do we keep, and can our team run the agents without you?”

The 12 best agentic AI companies in Singapore at a glance {#at-a-glance}

Each company below has a verified Singapore presence or Singapore delivery as of September 2026. The order reflects the strength of publicly documented action controls (permissions, approvals, logging and containment) against this guide’s criteria, not size or overall quality. Synthesised.ai publishes this guide; its placement rests on named, client-reported case studies, labelled as such.

Reviews of the 12 best agentic AI companies in Singapore {#reviews}

1. IBM (watsonx Orchestrate and IBM Consulting)

IBM is a global, governance-heavy technology and consulting company suited to regulated enterprises that want one control plane across many agents.

IBM’s agent platform, watsonx Orchestrate, added an Agentic Control Plane announced on 2 July 2026, which IBM describes as one place to control every agent: an agent access overview showing which agents can reach which integrations, policy management that enforces rules at runtime so agent behaviour stays within defined boundaries, content guardrails that block non-compliant outputs, and analytics on usage and reliability. IBM set out its Asia Pacific AI plan in Singapore on 21 July 2026, framed around orchestration-led governance.

• Runtime policy enforcement across agents in the Agentic Control Plane.
• Agent access overview mapping agents to integrations and data sources.
• Deployment on IBM Cloud, AWS or on-premises (IBM pricing page).

Pricing: Essentials from US$530 per month and Standard from US$6,360 per month, with Premium (including on-premises) on custom pricing, per IBM, September 2026. Consulting is custom priced.

Best for: Regulated enterprises standardising many agents, including third-party agents, under one policy layer with an on-premises option.

How does it compare? According to TrustRadius reviewers of watsonx Orchestrate (as of September 2026), common drawbacks include a steep learning curve and limited native connectors for specialised systems.

2. ServiceNow

ServiceNow is a workflow-native, enterprise-scale agent platform built for organisations that already run IT, HR or customer service on ServiceNow.

ServiceNow expanded its AI Control Tower on 5 May 2026 to discover, observe, govern, secure and measure AI across the enterprise, including the ability to detect and shut down agents that go off script or operate beyond their permissions. The same release lists Intelligent Approvals as generally available from May 2026. ServiceNow lists a Singapore office at Suntec Tower 4.

Top features

• Inventory of first-party and third-party agents, models and MCP servers (product page).
• Least-privilege access enforcement and prompt-injection blocking.
• Detect-and-shut-down monitoring for agents acting beyond permissions.

Pricing: Custom pricing; the AI Control Tower page directs buyers to contact ServiceNow (ServiceNow, September 2026).

Best for: Enterprises already running core workflows on ServiceNow that want agent governance inside the same platform and CMDB.

How does it compare? ServiceNow’s own product page states that AI Control Tower runs on the ServiceNow AI Platform and sits within the customer’s CMDB, so organisations without an existing ServiceNow estate adopt the platform as well as the governance layer.

3. UiPath

UiPath is an automation-led, orchestration-first agentic platform designed for enterprises extending existing robotic process automation into AI agents.

UiPath’s documentation describes custom tool guardrails that can log, filter, block or escalate an agent’s tool call when set conditions are met, and agent escalations that route decisions to a person for validation or approval through Action Center. UiPath lists a Singapore office at Marina Bay Financial Centre Tower 3.

Top features

• Tool-level guardrails with log, filter, block and escalate actions.
• Human-in-the-loop escalations through Action Center apps.
• Multi-region or on-premises deployment on the Enterprise plan (UiPath pricing).

Pricing: Community is free; Basic starts at US$25 per month (European region only) and includes limited agentic automation and limited agent consumption; Standard and Enterprise, which UiPath lists with agent use that scales without those limits, are priced through sales, per UiPath, September 2026.

Best for: Enterprises with an established automation estate that want agents to inherit existing process controls.

How does it compare? According to TrustRadius reviewers of the UiPath platform (as of September 2026), common drawbacks include high licensing costs and slow performance in Studio.

4. Synthesised.ai

Synthesised.ai is a build-led, governance-first production AI studio suited to Singapore and Australian enterprises that need agents to act in live systems under explicit approval rules.

Synthesised AI Pte. Ltd. is headquartered in Singapore and serves Singapore, Australia, New Zealand and South East Asia. It builds custom AI systems to production, including agents, conversational AI, integrations and workflow automation, and advises on adoption. Its delivery follows one practical approach, adapted to the business and use case: discover, prototype, productionise, with evaluation, observability and governance controls agreed for each engagement. Because we built the systems described below, we can describe their controls in more detail than the public record allows for other companies on this list.

The most directly relevant build is Perispark, a revenue-and-operations platform for a management consultancy advising local service businesses. It connects ad platforms through OAuth with hourly sync, runs a conversational ads analyst over a deterministic metric layer, and places an approve-before-execute layer in front of any AI action that touches a live budget or live site. Action proposals expire after 15 minutes, budget changes are capped at plus or minus 50%, and AI-executed actions are audit-logged (Synthesised.ai company records). Client-reported outcomes, as stated on the case study: about 200 consultant hours returned per month across a 20-client roster, and 5 to 10% of managed ad spend recovered.

A second build, RefinedAI, runs a scheduled multi-step pipeline unattended: brand-visibility checks across ChatGPT, Claude, Gemini and Perplexity move through check, analysis, snapshot and alert stages, and each check stores its prompt, model, response and a versioned methodology fingerprint, so any reported number is traceable to the question and method that produced it (RefinedAI case study). The client reports that a 40-check sweep across four AI assistants runs for about US$0.78 in model costs.

Outside client work, founder Kevin Dam builds and runs multi-step agents in production at Aemorph, his related search-visibility agency: a multi-phase content-architecture pipeline, an operational runbook agent and an entity-readiness conductor that orchestrates other agents. These systems are not publicly documented; the description comes from Synthesised.ai company records. For hosting, Synthesised.ai’s LLM MediaOS build runs its database and application servers in the Singapore region (AWS ap-southeast-1), per Synthesised.ai company records. Synthesised.ai’s Relay build runs AI replies on WhatsApp from supplied documents as the source of truth, with human handoff when the AI lacks the information; Relay is not on the case studies page, so this also comes from company records. Client outcomes for the named builds are on the case studies page.

Synthesised.ai builds to ISO/IEC 42001 practices and the NIST AI Risk Management Framework, and its data handling is PDPA-aware; current status is on the certifications and partnerships page. It has no third-party review profiles, so the outcomes above are client-reported rather than independently verified.

Top features

• Approve-before-execute action layer. High-risk actions stop as proposals for a named approver, and proposals expire if not approved in time.
• Hard value limits. Caps on the size of any AI-proposed change, set in code rather than in the prompt.
• Audit logging of executed actions. Every AI-executed action is recorded for review.
• Deterministic metric layers. Agents reason over calculated metrics, not model estimates.
• Traceable runs. Each check stores its prompt, model, response and a methodology fingerprint, so results can be audited.
• Scheduled multi-step pipelines. Agents that run unattended on a schedule with alerting at the end.
• Singapore-region hosting in a delivered build. LLM MediaOS runs its database and application servers in the Singapore region (AWS ap-southeast-1), per company records.
• Human handoff in conversational agents. Conversations pass to a person when the source documents do not cover the question.

Pricing: Synthesised.ai does not publish a rate card; fees are scoped through an enquiry.

Best for: Singapore and Australian enterprises that need a custom agent to act in live systems, with the permission, approval and logging design built around their own approvers.

How does it compare? Not built for multi-country integration programmes that need a large consulting bench, for strategy-only engagements (its work to date is build-led), or for buyers who want a licensed, off-the-shelf agent platform.

5. Salesforce Agentforce

Salesforce Agentforce is a CRM-native, consumption-priced agent platform built for customer service and sales teams whose records already live in Salesforce.

Salesforce announced a US$1 billion, five-year investment in Singapore on 11 March 2025, covering Agentforce adoption, the expansion of its Singapore AI Research hub (operating since 2019) and Hyperforce data residency for Agentforce. The same day, Salesforce announced that Singapore Airlines will use Agentforce to support customer case management in its customer service operations. Salesforce’s Trailhead guidance describes topics that scope agent behaviour and event logging of AI interactions.

Top features

• Topics, instructions and actions that scope agent behaviour.
• Einstein Trust Layer with zero data retention by third-party model providers and event logging.
• Singapore data residency through Hyperforce.

Pricing: US$2 per conversation, Flex Credits at US$500 per 100,000, or add-ons from US$125 per user per month, per Salesforce, September 2026.

Best for: Customer service and sales teams whose customer data and workflows already sit in Salesforce.

How does it compare? According to TrustRadius reviewers of Agentforce (as of September 2026), common drawbacks include a steep learning curve with extensive configuration options and a dependence on high-quality, structured data.

6. NCS

NCS is a Singapore-headquartered, public-sector-scale technology services group suited to government and regulated enterprises that need sovereign deployment of AI agents.

A Singtel Group company, NCS partnered with NVIDIA on 18 March 2026 to deliver agents that can understand requests, make decisions and take action, running on-premises or in the cloud while the organisation keeps control of sensitive data. On 9 July 2026 it launched its Sunshine platforms, including an agentic assistant and Sunshine.guardian, an AI safety engine that watches each agent, tests it by simulating attacks and fixes issues.

Top features

• Sovereign deployment on-premises or in the cloud.
• Sunshine.guardian agent monitoring and attack simulation.
• Sector focus on healthcare, financial services, telecommunications and the public sector.

Pricing: Custom pricing; NCS does not publish a rate card (NCS, September 2026).

Best for: Government agencies and regulated enterprises that need agents deployed inside their own environment by a Singapore integrator.

How does it compare? NCS’s own announcement dates the Sunshine launches to July 2026, so ask NCS for production references for the product in scope.

7. Deloitte

Deloitte is a multidisciplinary, risk-led professional services firm suited to enterprises that want agentic AI designed alongside governance, risk and cyber advisory.

Deloitte launched its Asia Pacific Agentic AI Centre of Excellence on 3 June 2025, bringing together more than 120 AI specialists supported by more than 6,000 practitioners across the region, with hubs reported in India, Malaysia and Singapore. Its Southeast Asia agentic AI services include governance and trust implementation and monitoring, founded on its Trustworthy AI framework.

Top features

• Zora AI domain agents built on NVIDIA AI.
• Governance and trust implementation and ongoing trust monitoring.
• Risk, governance and cyber advisory in the same engagement.

Pricing: Custom pricing; no public rate card (Deloitte, September 2026).

Best for: Enterprises whose board or regulator expects agent design and risk assurance to come from one adviser.

How does it compare? Deloitte’s launch release describes a regional centre of excellence rather than a Singapore-only team, so confirm with Deloitte which hub staffs and supports your engagement.

8. Accenture

Accenture is a global, ecosystem-spanning consulting and technology firm built for large enterprises coordinating agents across several software vendors.

Accenture announced an AI Refinery Engineering Hub in Singapore on 26 November 2024, supported by the Economic Development Board. Its Trusted Agent Huddle (28 April 2025) lets agents from different vendors work together using open protocols such as Agent2Agent and Model Context Protocol, with a certification process Accenture links to a future agent trust score.

Top features

• AI Refinery platform built on NVIDIA AI Enterprise.
• Cross-vendor agent interoperability through Trusted Agent Huddle.
• Agent evaluation and certification toward a trust score.

Pricing: Custom pricing; no public rate card (Accenture Singapore, September 2026).

Best for: Large enterprises running agents from several SaaS vendors that need one partner to coordinate them.

How does it compare? Accenture’s own November 2024 release describes the Singapore hub’s remit as applied research and custom model development, so confirm which team builds, governs and runs your agents day to day.

9. Temus

Temus is a Temasek-owned, deployment-focused digital transformation firm suited to Singapore financial services and health organisations moving agents from pilot to production.

Temus launched an AI Foundry in Singapore on 20 May 2026, supported by Digital Industry Singapore, and said the Foundry will hire, develop and deploy 50 Singapore-based AI professionals; CRN Asia reported plans to hire over 50. Reported work includes an agentic AI copilot for an investment company and a multi-agent vulnerability management solution for a security firm.

Top features

• LLM and agentic application builds (Temus).
• Internally hosted deployments for sensitive data.
• Singapore-based foundry team focused on production deployment.

Pricing: Custom pricing; no public rate card (Temus, September 2026).

Best for: Singapore financial institutions and health organisations that want a local team to take agents into production.

How does it compare? According to CRN Asia (May 2026), the foundry’s initial focus is financial services and precision health, so buyers in other sectors need to confirm relevant references.

10. WIZ.AI

WIZ.AI is a Singapore-founded, voice-first agent platform built for contact centres running high-volume, multilingual customer calls.

WIZ.AI, founded in 2019 and headquartered in Singapore, runs inbound and outbound voice agents with quality management that monitors every call for compliance, and hands complex cases to live agents. It states more than 300 enterprise clients across 17 countries (vendor-reported).

Top features

• Wizlynn inbound multi-agent service and Talkbot outbound calling.
• Quality management with compliance-driven call review.
• Handoff to live agents for complex cases.
• Vendor-stated SOC 2 Type II and ISO 27001 certifications.

Pricing: Custom pricing; no public price list (WIZ.AI, September 2026).

Best for: Contact centres and customer operations teams automating voice conversations at volume.

How does it compare? WIZ.AI’s own product pages centre on voice and call operations, so agents that act in back-office systems such as ERP or finance sit outside its documented scope.

11. AI Rudder

AI Rudder is a Singapore-headquartered, voice-led conversational agent company designed for banks, lenders and insurers automating collections, telemarketing and verification calls.

Founded in 2019, AI Rudder raised a US$50 million Series B in March 2022, naming Singapore as its headquarters. Its Singapore site lists voice agents for collections, telemarketing, customer service, KYC verification and quality assurance, with encryption, strict access controls and regular audits and monitoring (vendor-stated).

Top features

• Voice agents for collections, telemarketing and KYC verification.
• Quality assurance across conversations.
• Access controls, encryption and audit monitoring (vendor-stated).

Pricing: Custom pricing; no public price list (AI Rudder, September 2026).

Best for: Financial services teams running repetitive, scripted outbound and verification calls.

How does it compare? AI Rudder’s own Singapore site describes its agents in terms of voice and chat conversations, so agents that must act across internal systems fall outside its documented scope.

12. 41 Labs

41 Labs is a young, fully managed Singapore AI agency built for small and mid-sized service businesses that want a WhatsApp sales agent live quickly.

41 Labs’ main product, 41 Closer, answers enquiries on WhatsApp, generates quotes from product catalogues, follows up leads and takes payment, handing complex issues to staff. Its Clutch profile lists a Singapore address and AI agents among its service lines.

Top features

• WhatsApp sales agent that quotes, follows up and takes payment.
• Fully managed service.
• Handoff of complex issues to the client’s staff.

Pricing: From $690 per month, fully managed, with the currency not stated, per 41 Labs, September 2026; Clutch lists a minimum project size of $10,000.

Best for: Service, trading and e-commerce SMEs that want a managed sales agent on WhatsApp.

How does it compare? According to Clutch (as of September 2026), 41 Labs was founded in 2024 and has three reviews, so its public delivery record is short. Its own site states that amendments after a sale go to a person.

How to choose an agentic AI company: decision framework {#how-to-choose}

Role-based guidance

• For CIOs and CTOs: prioritise where the control layer lives. If it sits in a platform, confirm it covers every system your agents will touch. If you are still comparing platforms themselves, see our guide to the best enterprise AI platforms.
• For chief risk officers and heads of compliance: prioritise approve-before-execute checkpoints, hard limits and reconstructable run records, mapped to the four dimensions of IMDA’s agentic framework.
• For data protection officers: prioritise where model calls and logs are processed and stored, retention periods, and the PDPA roles of each party in the contract.
• For operations and business unit heads: prioritise one narrow, high-volume workflow with a measured baseline, so the first agent’s value is provable.
• For CFOs and procurement: prioritise cost limits per run and per agent, and the exit terms: what you keep if the contract ends.

Implementation considerations

Every choice here is a trade-off. Platform agents deploy faster but fit the platform’s data model; custom agents fit your actions closely but need a partner who hands over what it built. Tighter approval policies reduce risk but slow throughput, so most programmes start with more checkpoints and relax them as each agent earns trust on logged evidence. Sovereign deployment keeps data in your environment but adds hosting and maintenance work. Measure a baseline (time, cost and error rate per task) before the first agent ships, because every later claim of value depends on it. Contract terms matter as much as architecture: agree who is allowed to change the permission register and approval policy after go-live, how quickly the partner must respond to a paused agent, and what notice either side gives before a model or platform version changes, since any of these can alter what an agent does in live systems. Start with one agent on a narrow task, write the permission register and checkpoint policy before it goes live, and add the second agent only when the first has a clean run history.

Frequently asked questions {#faq}

How do you evaluate an agentic AI company? Evaluate how it limits what agents can do without approval: per-agent permissions, approve-before-execute checkpoints on irreversible actions, hard value limits, reconstructable run records and rollback. Ask for a demo of an action the agent was not allowed to take.

How much does agentic AI development cost in Singapore? Costs vary by delivery model. Platform vendors publish some prices, for example per conversation or per month, while integrators and custom builders usually price per engagement after scoping. Budget separately for platform licences, build work and model usage per run.

Is agentic AI regulated in Singapore? As of September 2026, Singapore has no standalone law specifically regulating agentic AI. IMDA’s Model AI Governance Framework for Agentic AI (Version 1.5, 20 May 2026) and CSA’s agentic security addendum (published 17 June 2026) are voluntary guidance, and generally applicable laws still govern how an agent is used. The PDPA applies to covered organisations whose agents handle personal data, with obligations that depend on each party’s role and the Act’s exceptions.

What is the difference between an AI agent and robotic process automation? Robotic process automation follows fixed, rule-based steps. An AI agent decides its own steps toward a goal and can handle variation, which is why it needs permissions, approvals and logging that fixed automation does not.

Should an AI agent ever act without human approval? Yes, for reversible, low-value, internal actions inside tested limits. Irreversible, high-value or customer-facing actions need to stop for a named approver until the agent has a logged track record on that action class.

Final word {#final-word}