Multi-Agent Orchestrator
We deploy a "Manager Agent" (using LangGraph or AutoGen) that breaks down complex goals into sub-tasks. It assigns work to "Worker Agents" (Researcher, Coder, Reviewer) and aggregates the results.
Agentic AI development
Empower your organization with smart agents designed to learn from your data and improve autonomously. We help you achieve tangible ROI by maximizing efficiency and significantly reducing operational spend.
Agentic AI development
Empower your organization with smart agents designed to learn from your data and improve autonomously. We help you achieve tangible ROI by maximizing efficiency and significantly reducing operational spend.
This is what you receive
We engineer Multi-Agent Systems (MAS) that act as digital employees. You receive a structured workforce with defined roles, tools, and strict "Human-in-the-Loop" checkpoints.
The "Tool Use" Library
Agents are useless without hands. We code specific "Tools" (Python functions) that allow the AI to safely query your SQL database, send Slack messages, or scrape the web. Capabilities are defined by code, not hallucinations.
Safe Execution
API Integration
Human-in-the-Loop Protocol
For high-stakes actions (e.g., "Send Refund"), the Agent is hard-coded to pause and request human approval. We build the UI where humans review the Agent's proposed plan before execution.
Approval Gates
Safety First
Chain-of-Thought Audit Logs
"Why did the Agent do that?" We provide granular logs of the Agent's internal reasoning steps. You can audit exactly which logic path it took to arrive at a decision, ensuring full explainability.
Full Traceability
Debuggable Logic
State Management Database
Agents need long-term memory. We implement a persistent state layer (Postgres/Redis) that allows agents to "pause" a task today, wait for user input, and resume exactly where they left off next week.
Persistent Memory
Async Workflows
Multi-Agent Orchestrator
We deploy a "Manager Agent" (using LangGraph or AutoGen) that breaks down complex goals into sub-tasks. It assigns work to "Worker Agents" (Researcher, Coder, Reviewer) and aggregates the results.
Hierarchical Control
Task Delegation
The "Tool Use" Library
Agents are useless without hands. We code specific "Tools" (Python functions) that allow the AI to safely query your SQL database, send Slack messages, or scrape the web. Capabilities are defined by code, not hallucinations.
Safe Execution
API Integration
Human-in-the-Loop Protocol
For high-stakes actions (e.g., "Send Refund"), the Agent is hard-coded to pause and request human approval. We build the UI where humans review the Agent's proposed plan before execution.
Approval Gates
Safety First
Chain-of-Thought Audit Logs
"Why did the Agent do that?" We provide granular logs of the Agent's internal reasoning steps. You can audit exactly which logic path it took to arrive at a decision, ensuring full explainability.
Full Traceability
Debuggable Logic
State Management Database
Agents need long-term memory. We implement a persistent state layer (Postgres/Redis) that allows agents to "pause" a task today, wait for user input, and resume exactly where they left off next week.
Persistent Memory
Async Workflows
A specialized agent for every business challenge
Real-world operations require more than a one-size-fits-all approach. Whether you need instant industrial safety responses or multi-tiered workflow orchestration, our bespoke architectures ensure the right level of cognitive control for every task.
Self-evolving learning agents
Step beyond static algorithms. Our agents are designed to mature over time, refining their decision-making logic by actively analyzing user feedback and instruction nuance. Through continuous performance self-auditing, they become sharper and more autonomous with every interaction, all while keeping critical control firmly in human hands.
Utility-driven decision engines
In complex environments where every choice counts, our utility-based agents excel. Beyond simply completing tasks, they evaluate the quality of the outcome. By running advanced predictive simulations, these agents calculate the optimal path to maximize value—helping you balance immediate operational needs with long-term strategic goals while effectively mitigating risk.
Autonomous workflow orchestration
Metanow’s goal-based agents are engineered to handle intricate, multi-stage processes with complete autonomy. Powered by advanced planning algorithms and Large Language Models (LLMs), these agents don't just follow a script—they continuously calculate the optimal path to reach your specific objectives. Even when operational conditions shift, our systems adapt instantly to keep your business targets on track.
Context-aware industrial intelligence
For complex environments where blind reaction isn't enough, Metanow deploys model-based reflex agents. These systems maintain a dynamic "internal state"—effectively a digital twin of your operation—to track changes over time. By combining live telemetry with historical data, they don't just react to the moment; they understand the bigger picture, making them essential for high-stakes industrial simulations and decision-making.
Instant action for critical events
When milliseconds matter, our simple reflex agents deliver immediate results. Designed for high-speed environments, these agents monitor data streams in real-time to detect anomalies the moment they occur. By utilizing advanced pattern recognition, they instantly execute pre-validated protocols—making them the perfect solution for predictive maintenance and emergency system shutdowns where hesitation is not an option.
Multi-tiered intelligence orchestration
Tackle your most sophisticated business challenges with a structured digital workforce. Our hierarchical agents operate in coordinated tiers, functioning much like a high-performance management team. The top-level "manager" agents oversee intricate processes across different business functions, strategically decomposing complex workflows into smaller tasks for specialized "child agents" to execute. This ensures maximum efficiency while maintaining strict accountability and performance tracking at every level of the chain.
Self-evolving learning agents
Step beyond static algorithms. Our agents are designed to mature over time, refining their decision-making logic by actively analyzing user feedback and instruction nuance. Through continuous performance self-auditing, they become sharper and more autonomous with every interaction, all while keeping critical control firmly in human hands.
Utility-driven decision engines
In complex environments where every choice counts, our utility-based agents excel. Beyond simply completing tasks, they evaluate the quality of the outcome. By running advanced predictive simulations, these agents calculate the optimal path to maximize value—helping you balance immediate operational needs with long-term strategic goals while effectively mitigating risk.
Autonomous workflow orchestration
Metanow’s goal-based agents are engineered to handle intricate, multi-stage processes with complete autonomy. Powered by advanced planning algorithms and Large Language Models (LLMs), these agents don't just follow a script—they continuously calculate the optimal path to reach your specific objectives. Even when operational conditions shift, our systems adapt instantly to keep your business targets on track.
Context-aware industrial intelligence
For complex environments where blind reaction isn't enough, Metanow deploys model-based reflex agents. These systems maintain a dynamic "internal state"—effectively a digital twin of your operation—to track changes over time. By combining live telemetry with historical data, they don't just react to the moment; they understand the bigger picture, making them essential for high-stakes industrial simulations and decision-making.
Instant action for critical events
When milliseconds matter, our simple reflex agents deliver immediate results. Designed for high-speed environments, these agents monitor data streams in real-time to detect anomalies the moment they occur. By utilizing advanced pattern recognition, they instantly execute pre-validated protocols—making them the perfect solution for predictive maintenance and emergency system shutdowns where hesitation is not an option.
Multi-tiered intelligence orchestration
Tackle your most sophisticated business challenges with a structured digital workforce. Our hierarchical agents operate in coordinated tiers, functioning much like a high-performance management team. The top-level "manager" agents oversee intricate processes across different business functions, strategically decomposing complex workflows into smaller tasks for specialized "child agents" to execute. This ensures maximum efficiency while maintaining strict accountability and performance tracking at every level of the chain.
Industry applications of AI agent development
Tailored custom AI solutions designed to meet the unique challenges of your sector.
Genomic Analysis
Smart Inventory
Smart Streaming
Predictive Trading
Fraud Detection
Supply Chain Twin
Predictive Maintenance
Seismic Imaging
Healthcare
Genomic Analysis
Smart Inventory
Smart Streaming
Predictive Trading
Fraud Detection
Supply Chain Twin
Predictive Maintenance
Seismic Imaging
Healthcare
This is a simple example use case for an AI agent system.
Metanow’s multi-agent system (MAS) automates the RFQ response lifecycle. By orchestrating specialized LLM agents to synthesize live specialist availability, historical pricing vectors, and predictive estimations, the system engineers precise commercial quotes instantly.
Ingest
Multi-Channel Input
Captures raw RFQ (PDF, Docx, Email) via Webhooks.
Data Store
Rate Card DB
Live consultant hourly rates and availability.
Security Protocol
Sensitive Info Masking
Detects and masks PII before context passing.
Vector Store
Historical DB
Vectorized database of past winning proposals.
Agent: Cost
Cost Calc Engine
Maps requirements to internal resource costs.
Agent: Writer
Proposal Draft Agent
Synthesizes cost and strategy into draft.
Agent: Strategy
Pricing Optimization
Adjusts margins based on win/loss probability.
Human in the Loop
Executive Review
Validation of margins. Trigger regeneration if rejected.
Deliverable
Final Package
Re-integrates sensitive data. Exports PDF/JSON.
Ingest
Multi-Channel Input
Captures raw RFQ (PDF, Docx, Email) via Webhooks.
Data Store
Rate Card DB
Live consultant hourly rates and availability.
Security Protocol
Sensitive Info Masking
Detects and masks PII before context passing.
Vector Store
Historical DB
Vectorized database of past winning proposals.
Agent: Cost
Cost Calc Engine
Maps requirements to internal resource costs.
Agent: Writer
Proposal Draft Agent
Synthesizes cost and strategy into draft.
Agent: Strategy
Pricing Optimization
Adjusts margins based on win/loss probability.
Human in the Loop
Executive Review
Validation of margins. Trigger regeneration if rejected.
Deliverable
Final Package
Re-integrates sensitive data. Exports PDF/JSON.
MAS modules explained
Ingest
RFQ Intake & Security
Raw files (PDF/Docx) pass through a Sensitive Info Adapter. Simultaneously, RFQs arrive via web or email—filtered automatically to protect confidentiality.
Structure
Context Extraction
An LLM agent processes the inputs, extracting structured technical requirements. These details are indexed against our Internal Resource DB for matching.
Analysis
RFQ Decomposition
Agents break down the RFQ into line items and match them against the Historical Vector DB to identify similar past winning quotes and scope patterns.
Estimation
Costing & Logic
Semantic matching pairs requirements with available specialists. A regression-based estimator then calculates effort and margin logic to generate the draft price.
Delivery
Quote Submission
The system compiles the commercial quote. SMEs validate the pricing and scope before the agent packages the PDF and handles the email submission.
Ingest
RFQ Intake & Security
Raw files (PDF/Docx) pass through a Sensitive Info Adapter. Simultaneously, RFQs arrive via web or email—filtered automatically to protect confidentiality.
Structure
Context Extraction
An LLM agent processes the inputs, extracting structured technical requirements. These details are indexed against our Internal Resource DB for matching.
Analysis
RFQ Decomposition
Agents break down the RFQ into line items and match them against the Historical Vector DB to identify similar past winning quotes and scope patterns.
Estimation
Costing & Logic
Semantic matching pairs requirements with available specialists. A regression-based estimator then calculates effort and margin logic to generate the draft price.
Delivery
Quote Submission
The system compiles the commercial quote. SMEs validate the pricing and scope before the agent packages the PDF and handles the email submission.
Our delivery methodology
We don't just build software; we engineer intelligent ecosystems. Our five-stage framework ensures that your AI agents are not only technically robust but seamlessly integrated into your existing business logic for maximum operational impact.
Swipe to view steps →
Phase 01
Ecosystem Discovery & Assessment
Deep-dive Landscape Analysis
Audit Data & Software Stack
Identify Integration Points
Completed
Phase 02
Strategic Solution Architecture
Implementation Blueprint
Data Flow Mapping
Config Agent Personas
Completed
Phase 03
Development, Training & Security
Build Tailored Agents
Enterprise Security Protocols
Agile Behavior Refinement
In Progress
Phase 04
Seamless Integration & Deployment
Phased Deployment Strategy
System Synchronization
Data Protection Standards
Planned
Phase 05
Continuous Optimization & Scaling
Performance Monitoring
Fine-tune Decision Logic
Team Training & Support
Scheduled
Swipe to view steps →
Phase 01
Ecosystem Discovery & Assessment
Deep-dive Landscape Analysis
Audit Data & Software Stack
Identify Integration Points
Completed
Phase 02
Strategic Solution Architecture
Implementation Blueprint
Data Flow Mapping
Config Agent Personas
Completed
Phase 03
Development, Training & Security
Build Tailored Agents
Enterprise Security Protocols
Agile Behavior Refinement
In Progress
Phase 04
Seamless Integration & Deployment
Phased Deployment Strategy
System Synchronization
Data Protection Standards
Planned
Phase 05
Continuous Optimization & Scaling
Performance Monitoring
Fine-tune Decision Logic
Team Training & Support
Scheduled
Related services: AI-native operations · AI consulting · Intelligent automation