.// HOW IT WORKS

From question to governed action. Continuous.

Every interaction follows a four-stage pipeline: assemble context, reason through the semantic layer, execute with governance, and monitor for continuous improvement. No black boxes.

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4

Pipeline Stages

26

OS Modules

42

AI Agents

1,607

RLS Policies

.// DATA FLOW

Four stages. Zero black boxes. Full traceability.

Every agent request follows an identical pipeline — whether it is a simple question or a multi-step autonomous workflow. Here is exactly what happens.

1

Context Assembly

The Context Engine receives the request and assembles everything the agent needs to respond: live data from 920+ tables, document embeddings from the RAG knowledge base, user permissions from RBAC policies, and relationship context from the semantic graph.

What happens at this stage

  • 1. Request parsed and intent classified
  • 2. Relevant data sources identified from 920+ tables
  • 3. RAG retrieval pulls relevant document chunks
  • 4. Entity graph resolves cross-system relationships
  • 5. User permissions loaded from 1,607 RLS policies
2

Semantic Reasoning

The Semantic Layer takes the assembled context and reasons through it. The LLM generates an execution plan — a structured sequence of actions, each checked against permission boundaries before it can proceed. If the request involves sensitive operations, human-in-the-loop approval gates activate.

What happens at this stage

  • 1. LLM generates structured execution plan
  • 2. Each action verified against RBAC policies
  • 3. Confidence scored per action step
  • 4. Sensitive operations flagged for human approval
  • 5. Rollback strategies defined for each step
3

Governed Execution

The Action Engine executes the approved plan step by step. Each action is logged with timestamps, user context, and reasoning chains. Results are verified, and the complete audit trail is written to the governance layer. Failures trigger automatic rollback.

What happens at this stage

  • 1. Actions execute in dependency order
  • 2. Cross-system writes via governed connectors
  • 3. Results verified against expected outcomes
  • 4. Full audit trail with decision rationale
  • 5. Context graph updated with new state
4

Continuous Observability

The Developer Layer (Forge) monitors every execution in real-time. Reasoning traces are analyzed for performance, token costs are aggregated for ROI tracking, and feedback loops are created to refine agent behavior and tool selection for future requests.

What happens at this stage

  • 1. Real-time tracing of reasoning chains
  • 2. Performance metrics and latency analysis
  • 3. Token cost aggregation per-agent and per-org
  • 4. Error analysis and automated debugging logs
  • 5. Feedback integrated into prompt/tool optimization

.// MCP PROTOCOL

Open protocol. Extensible by default.

agints implements the Model Context Protocol for tool and data integration. Every connector, every data source, and every action surface is exposed as an MCP-compatible server — making agints extensible by default.

MCP Tool Servers

Each integration exposes its capabilities as MCP tools. Agents discover available actions dynamically and compose multi-tool workflows without hardcoded logic.

MCP Data Sources

Enterprise data surfaces as MCP resources. Agents query structured data, retrieve documents, and traverse the entity graph through a unified protocol.

Custom MCP Servers

Build your own MCP servers to expose proprietary systems. agints treats custom integrations identically to built-in connectors — same governance, same audit trails.

.// MODEL ARCHITECTURE

Self-hosted inference. Your data never leaves your environment.

agints runs a self-hosted inference stack with Qwen 3.5-9B as the primary model, Claude as an API fallback for complex reasoning tasks, and a future agint 1-9b fine-tuned for enterprise operations.

Primary

Qwen 3.5-9B

Self-hosted on your infrastructure or agints. Handles 90%+ of inference requests with zero external API calls. Your data never leaves your environment.

Fallback

Claude API

For complex reasoning tasks that exceed the primary model's capabilities. Zero-retention API with no data used for training. Opt-in per workspace.

Roadmap

agint 1-9b

A custom model fine-tuned for enterprise operations — optimized for structured data reasoning, workflow planning, and multi-step execution at 9B parameter efficiency.

.// KNOWLEDGE BASE

Grounded-md in your data. Not hallucinated.

Every agent response is grounded-md in your actual enterprise data through Retrieval-Augmented Generation. Documents, policies, contracts, and runbooks are embedded and indexed for semantic retrieval — not hallucinated from training data.

Document Ingestion

Upload PDFs, Word documents, Confluence pages, Notion databases, and Google Docs. agints chunks, embeds, and indexes them for semantic retrieval.

Semantic Retrieval

When an agent needs context, it retrieves the most relevant document chunks based on semantic similarity — not keyword matching. Results include source citations.

Structured + Unstructured Fusion

Agents combine RAG results with live structured data from 920+ tables. A support agent references both the knowledge base article and the customer's actual account data.

Tenant-Isolated Indexes

Each organization's knowledge base is completely isolated. Embeddings, indexes, and retrieval boundaries are enforced at the tenant level. No cross-contamination.

.// GOVERNANCE

1,607 policies enforcing every action. Every decision.

agints' governance layer is not a feature — it is the foundation. 1,607 row-level security policies, complete audit trails, human-in-the-loop approval gates, and per-agent cost tracking ensure every AI action meets enterprise compliance standards.

Row-Level Security

1,607 RLS policies enforce data access at the row level. Agents only see what the user is permitted to see.

Audit Trails

Every action, query, and decision is logged with timestamps, user context, reasoning chains, and outcome verification.

Approval Gates

Sensitive operations require human approval before execution. Configurable per action type, department, and risk level.

Cost Tracking

Per-agent, per-action cost monitoring. Know exactly what each AI operation costs and optimize usage in real time.

.// READY TO DEPLOY?

Your competitors deployed AI agents last quarter. What's your timeline?

See how agints puts AI agents into production across Finance, Sales, Support, HR, and Compliance — with governance your enterprise requires. Start with a 30-minute discovery call.

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See how it works

Context Engine, Semantic Layer, Action Engine, and Developer Layer — see the four-layer architecture that powers governed agent execution.

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