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.
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.
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