Every agentic AI system runs a tight execution loop — from perceiving inputs to delivering results — automatically re-planning at each error node.
Every agent runs on a composable stack. Swap, extend, or instrument any layer without touching the others.
Ingests raw inputs — text, vision, structured data, tool calls
Chain-of-thought, ReAct, and tree-of-thought planners
Executes tool calls, HTTP requests, and sub-agent spawns
Short-term working memory + long-term vector recall
Multi-agent coordination, task delegation, consensus
Trace every loop, token, and tool call in real-time
# Build the full 6-layer stack
from aigentic import AgenticStack
stack = AgenticStack(
# L1 – Perception
perception=["text", "vision", "tools"],
# L2 – Reasoning
reasoner="react_cot", max_loops=12,
# L3 – Actions
tools=[search, calendar, crm],
# L4 – Memory
memory=VectorStore(dim=1536),
# L5 – Orchestration
orchestrator=Supervisor(
workers=["data", "analyst", "writer"]
),
# L6 – Observability
tracer=OTelTracer(endpoint=TRACE_URL)
)
result = await stack.run(goal="Analyse Q2 pipeline")from aigentic import Agent, Layer
# Assemble the 6-layer agent stack
agent = Agent(
perception = Layer.perception(sources=["text", "api"]),
memory = Layer.memory(backend="qdrant"),
reasoning = Layer.reasoning(model="claude-opus"),
action = Layer.action(tools=[search, crm, db]),
)
# Start the autonomous execution loop
result = await agent.run("Analyse Q2 sales trends")Six decoupled layers — perception, memory, reasoning, action, observation, orchestration — each independently swappable and extensible.
Connect any REST API, Python function, or MCP service to your agent's tool chain via a type-safe tool registration interface.
Built-in vector store, short-term working memory, and long-term knowledge base — agents maintain coherent context across sessions.
Every reasoning step, tool call, and decision node generates structured logs — seamlessly integrates with OpenTelemetry and major monitoring platforms.
The AI·GENTIC framework powers agentic AI across Malaysia — validated in production in multiple industries, giving developers ready-to-use agent templates.
Build agents that autonomously handle product recommendations, inventory scheduling, dynamic pricing, and after-sales service — end-to-end without human intervention.
Deploy autonomous analysis agents that collect market data in real time, analyse risk exposure, generate compliance reports, and trigger trading signals.
Use the AI·GENTIC framework to build custom AI tools, code-review agents, documentation generators, and automated test pipelines — accelerating your dev workflow.
Data encrypted in transit (TLS 1.3) and at rest (AES-256); agents run in isolated sandboxes and sensitive data never leaves your infrastructure.
Agents plug into invoicing and finance workflows — extracting, validating and delivering data automatically, cutting repetitive manual work.
On-premise and private deployment (Docker/Kubernetes) keeps business-critical data on infrastructure you control.
Malaysia is rolling out its National AI Office agenda — companies deploying agentic AI now get ahead of the curve.
| Capability | Chatbot | RPA Script | Aigentic Agent |
|---|---|---|---|
| Operating model | One Q&A at a time | Fixed scripted replay | Goal-driven, self-planned |
| Tool / API calls | Limited | Pre-configured | Dynamically chosen & orchestrated |
| Exception handling | Escalates to human | Fails and stops | Auto-retries and self-corrects |
| Multi-task coordination | None | None | Multi-agent (A2A) coordination |
| Auditability | Chat log | Operation log | End-to-end execution trail |
Agents monitor production-line data, generate quality reports, coordinate scheduling and chase suppliers — running 24/7 without hand-holding.
Handle restocking recommendations, dynamic promotions, customer service and loyalty operations across every store and channel with one agent network.
Deploy multi-agent networks for reconciliation, IT tickets, compliance reporting and knowledge-base Q&A — Agent-to-Agent (A2A) coordination at enterprise scale.
Look past demos: demand proof of observability (OpenTelemetry), health checks and graceful shutdown.
Confirm PDPA support, data-residency options and sandbox isolation.
The framework should support Claude / GPT / local Ollama — switchable per task, not locked to one vendor.
Complex workflows need A2A coordination and an event bus, not a single bot in a wrapper.
A Malaysian team, BM/English/Chinese support, and an SLA you can actually sign.
Agents read invoice data, validate fields, submit to LHDN MyInvois and write the status back — turning monthly reconciliation from days into hours, with a full audit trail at every step.
Direct connections to SAP, SQL Server, MySQL or any REST API. Agents read and write under role-based permissions, replacing the copy-paste shuffle between systems your staff does today.
Agents answer customers 24/7 across WhatsApp, website chat and email, qualify leads, and hand high-value conversations to your human team seamlessly — with full conversation logs.
Agents consolidate data from Google Sheets, Microsoft 365 and shared drives, then generate daily and weekly reports that write themselves — delivered to management automatically.
Generate audit-ready evidence packs on demand: who approved what, which tool ran when, and what data moved. Answer auditor and regulator questions in minutes, not weeks of manual log archaeology.
Designed against Malaysia's Personal Data Protection Act: minimal data collection, defined processing purpose, export-and-delete request workflows, and no sensitive data shipped to third-party clouds by default.
Every agent runs in an isolated sandbox with least-privilege access to tools and APIs. Every tool call passes a policy check and rate limit before it executes.
Every plan, every tool call and every output is recorded in an append-only execution log — when anything goes wrong, you can answer exactly what happened and why.
On-premise and private-cloud deployment (Docker/Kubernetes) keeps business data entirely on infrastructure you own, never routing through overseas servers.
High-stakes actions — payments, contract sends, bulk emails — require a named human approval before the agent proceeds. Autonomy where it helps; a checkpoint where it matters.
PDPA (2012) plus the IMDA Model AI Governance Framework: agents must keep decision logs and explainability. Aigentic's end-to-end execution trail satisfies the framework's accountability requirements out of the box — built for finance and logistics teams.
UU PDP No. 27/2022 is in force and data processors must document and report breaches. Structured audit logs turn compliance evidence into "export the log" instead of "reconstruct the timeline by hand".
PDPA (2562 B.E. / 2019) requires explicit consent and data-subject rights. Aigentic permission boundaries and consent workflows can be scoped down to individual tool calls.
Decree 13/2023 requires assessments for cross-border data transfers. On-premise deployment makes data residency an architectural default, not a retrofit.
Pick one high-frequency repetitive process — invoice processing, support tickets, report consolidation. We build a pilot agent wired to your real data and tools and verify end-to-end output quality.
Add guardrails to the pilot: permission boundaries, audit logging, retry policies and human approval points. Train your internal team and agree on acceptance criteria.
Replicate the proven pattern to the second and third process; establish multi-agent coordination and one unified monitoring dashboard. By now you have a measurable ROI baseline.
REST API, Python SDK, MCP Protocol, and Event Webhooks — plug into your stack from day one.
Call agents via a standard REST interface — streaming every intermediate reasoning step and final result in real time.
Tell us about your development needs and we'll provide dedicated framework access and technical support.
What agentic AI is, how it differs from a chatbot, what it costs in Malaysia — plus everything about the Aigentic framework, deployment, and pricing.
Our technical team typically responds within one business day.