Technical Overview

Technical Overview of Altan Agent Infrastructure
This document provides a detailed technical overview of the Altan Agent Infrastructure, focusing on its architecture, security, and scalability. The system is designed to integrate any Large Language Model (LLM) using a custom-built framework, enabling intelligent automation and authenticated interactions with third-party applications. Deployed as a system of three microservices on Google Kubernetes Engine (GKE), the infrastructure ensures flexibility, security, and scalability for multi-tenant AI-driven workflows, independent of database-specific features like Row-Level Security (RLS) or AlloyDB.
Architecture Overview
The Altan Agent Infrastructure is a cloud-native platform composed of three microservices, each running as a containerized workload in GKE. These microservices work together to orchestrate AI agents, execute tools, and integrate with third-party applications. The system is designed to be database-agnostic, focusing solely on agent-driven automation and external integrations.
Core Components

Agent Service: Manages the lifecycle of AI agents, including task ingestion, LLM interactions, and task decomposition.
Tool Service: Hosts and executes modular tools that agents use to perform specific actions, such as API calls or data processing.
Integrator Engine: Orchestrates interactions between agents, tools, and third-party applications, handling authentication and task coordination.

Each microservice is deployed in GKE as a stateless, scalable pod, with communication facilitated through gRPC and REST APIs. The infrastructure leverages Google Cloud’s ecosystem for security, monitoring, and scalability, ensuring robust performance for multi-tenant applications.
Agent Service
The Agent Service is responsible for managing AI agents, which are intelligent entities powered by LLMs. Key features include:

LLM-Agnostic Framework: The service integrates with any LLM (e.g., GPT, Llama, or proprietary models) via a standardized API. Tenants can configure their preferred LLM through a JSON-based configuration, specifying endpoints, authentication, and model parameters.{
"llm_id": "gpt-4",
"endpoint": "https://api.openai.com/v1/chat/completions",
"auth": {"type": "api_key", "key": "stored_in_secret_manager"},
"parameters": {"max_tokens": 4096, "temperature": 0.7}
}

Task Processing: Agents receive tasks via natural language inputs or structured JSON payloads, decompose them into subtasks, and invoke tools via the Integrator Engine. For example, a task to "send a Slack notification for a new lead" involves parsing the task, identifying the required tool, and coordinating execution.
Context Management: The service maintains task context (e.g., user inputs, session data) in memory or an external cache (e.g., Google Cloud Memorystore), ensuring stateful workflows without relying on a database.
Multi-Tenant Isolation: Each agent operates within a tenant-specific namespace in GKE, with task execution scoped to tenant-specific configurations and credentials.

Tool Service
The Tool Service hosts modular, reusable functions that agents call to perform actions, such as interacting with third-party APIs or processing data. Key features include:

Tool Library: A collection of pre-built tools for common tasks, including:
API Tools: Authenticated HTTP requests to third-party services (e.g., Salesforce, Slack, Google Workspace).
Utility Tools: Data parsing, text generation, or notification sending.

Custom Tools: Tenants can define custom tools using a JSON schema, specifying inputs, outputs, and execution logic. For example:{
"tool_id": "slack_notify",
"name": "Send Slack Notification",
"description": "Posts a message to a Slack channel",
"parameters": [
{"name": "channel_id", "type": "string"},
{"name": "message", "type": "string"}
],
"endpoint": "https://slack.com/api/chat.postMessage",
"method": "POST",
"auth": "oauth2"
}

Containerized Execution: Each tool runs as a lightweight container in GKE, allowing independent scaling and updates. Tools are implemented in languages like Python or Node.js, with execution triggered via gRPC calls from the Integrator Engine.
Extensibility: Developers can deploy new tools as microservices, with GKE’s service discovery ensuring seamless integration with the Agent Service.

Integrator Engine
The Integrator Engine is the orchestration layer that coordinates interactions between agents, tools, and third-party applications. It ensures secure, authenticated, and efficient task execution. Key features include:

LLM Orchestration: The engine abstracts LLM interactions, handling prompt construction, response parsing, and error recovery. It supports dynamic LLM switching based on tenant preferences or task requirements.
Authentication Management: The engine manages credentials for third-party APIs using Google Cloud Secret Manager. Supported auth methods include OAuth2, API keys, and custom tokens, with credentials isolated per tenant.
Task Coordination: The engine decomposes complex tasks into subtasks, maps them to appropriate tools, and sequences execution. For example, a task to "create a Google Calendar event from an email" involves:
Parsing the email (utility tool).
Calling the Google Calendar API (API tool).

Error Handling: The engine implements retry logic for transient failures (e.g., network issues, rate limits) and fallback mechanisms for unavailable tools. Errors are logged for debugging and monitoring.
Logging: Task execution details (e.g., tenant ID, tool invoked, API response) are logged to Google Cloud Logging for auditability, without relying on a database.

Security Mechanisms
Security is a core focus of the Altan Agent Infrastructure, ensuring tenant isolation, secure third-party interactions, and robust access control across the three microservices.

Tenant Isolation

GKE Namespaces: Each tenant is assigned a dedicated Kubernetes namespace, isolating Agent Service, Tool Service, and Integrator Engine instances. This prevents cross-tenant resource access or interference.
JWT-Based Authentication: All requests to the microservices are authenticated using tenant-specific JWTs, which encode tenant_id, user_id, or role. The Integrator Engine validates JWTs before processing tasks.
Credential Isolation: Third-party API credentials are stored in Google Cloud Secret Manager, encrypted with tenant-specific keys. The Integrator Engine ensures that tools access only the credentials associated with the requesting tenant.
Network Segmentation: GKE network policies restrict communication between microservices to authorized endpoints, with private IP-based routing within the tenant’s Virtual Private Cloud (VPC).

Secure Third-Party Interactions

OAuth2 and API Keys: The Integrator Engine manages OAuth2 flows (token issuance, refresh, revocation) and API key-based authentication for third-party services. Tokens are scoped to specific actions (e.g., read-only access to a CRM) to minimize risk.
Egress Control: All outbound API calls are routed through GKE’s private network, with VPC firewall rules restricting traffic to approved third-party endpoints.
Rate Limiting: The Integrator Engine enforces rate limits for third-party APIs, dynamically adjusting based on service quotas to prevent abuse or throttling.
TLS Encryption: All communication between microservices and external APIs uses TLS 1.3, ensuring data confidentiality and integrity.

Microservice Security

RBAC in GKE: Each microservice operates under a specific Kubernetes service account with least-privilege access. For example, the Tool Service can only invoke tools, while the Integrator Engine has access to Secret Manager.
Pod Security Policies: GKE enforces strict pod security standards, such as non-root containers and read-only filesystems, to minimize vulnerabilities.
Service Mesh (Optional): For enhanced security, Altan can deploy Istio to enforce mutual TLS (mTLS) between microservices, ensuring encrypted and authenticated intra-service communication.

Auditability and Monitoring

Audit Logs: All agent actions, tool invocations, and third-party API calls are logged to Google Cloud Logging, with metadata (e.g., tenant ID, timestamp, action type) for traceability.
Monitoring: GKE integrates with Google Cloud Monitoring to track microservice performance (e.g., CPU/memory usage, request latency) and detect anomalies (e.g., excessive API calls). Alerts are configured for suspicious activities.
Compliance: The infrastructure adheres to GDPR, HIPAA, and SOC2 standards, leveraging GKE’s compliance certifications.

Scalability Features
The Altan Agent Infrastructure is designed to scale seamlessly with growing workloads, from small-scale automations to enterprise-grade AI systems.

GKE Scalability

Horizontal Pod Autoscaling (HPA): Each microservice (Agent Service, Tool Service, Integrator Engine) scales independently based on CPU, memory, or custom metrics (e.g., task queue length). For example, the Tool Service can add pods to handle increased API call volumes.
Cluster Autoscaler: GKE dynamically adjusts the number of nodes in the cluster to meet resource demands, ensuring efficient utilization across all microservices.
Multi-Zone Deployment: Microservices are deployed across multiple availability zones within a GCP region, providing high availability and fault tolerance.

Integrator Engine Scalability

Stateless Design: The Integrator Engine is stateless, allowing multiple instances to run behind a Google Cloud Load Balancer to handle high task volumes.
Task Queuing: For high-throughput workloads, the engine uses Google Cloud Tasks to manage asynchronous task execution, preventing bottlenecks and ensuring fair resource allocation.
Parallel Execution: The engine supports parallel invocation of independent tools within a task, leveraging GKE’s container orchestration for efficient scaling.

Tool Service Scalability

Containerized Tools: Each tool runs in its own container, allowing independent scaling based on usage. For example, a frequently used Slack notification tool can scale to multiple pods without affecting other tools.
Service Discovery: GKE’s service discovery ensures that the Integrator Engine can dynamically route requests to available tool instances, supporting load balancing and failover.
Caching: Responses from third-party APIs are cached in Google Cloud Memorystore, reducing latency and API costs for repetitive tasks.

Global Scalability

Cross-Region Deployment: For global tenants, microservices can be deployed across multiple GCP regions, with traffic routed via Google Cloud’s global load balancer to minimize latency.
Regional Tool Endpoints: The Integrator Engine routes API calls to region-specific third-party endpoints (e.g., Salesforce’s EU servers) to optimize performance for geographically distributed users.

Integration with Third-Party Applications
The Altan Agent Infrastructure excels at enabling agents to perform authenticated actions in third-party applications. Examples include:

CRM Systems (e.g., Salesforce, HubSpot): Agents can update leads, create opportunities, or fetch customer data using OAuth2-authenticated API calls.
Collaboration Tools (e.g., Slack, Microsoft Teams): Agents can send notifications, create channels, or retrieve messages based on tenant-specific configurations.
Productivity Suites (e.g., Google Workspace, Microsoft 365): Agents can schedule meetings, create documents, or manage emails, all while respecting tenant boundaries.
Custom APIs: Tenants can define tools for proprietary APIs, with the Integrator Engine handling authentication and request orchestration.

Conclusion
The Altan Agent Infrastructure, deployed as three microservices (Agent Service, Tool Service, Integrator Engine) on GKE, provides a secure, scalable, and flexible platform for AI-driven automation. By leveraging a custom LLM integration framework, modular tools, and robust authentication mechanisms, Altan enables agents to perform complex, tenant-isolated actions across third-party applications. With GKE’s autoscaling, task queuing, and global deployment capabilities, the infrastructure supports workloads of any scale, from small automations to enterprise-grade systems.
Happy building with Altan Agents! 🚀