Orchestrated Intelligence for Complex Workflows
Single AI models are powerful. Orchestrated teams of specialised agents are transformational. We design and build multi-agent systems where each agent handles what it does best, coordinated to deliver results no single model could achieve.
Beyond Single-Model AI
The first wave of enterprise AI was chat interfaces to large language models. Useful, but limited. Real business processes involve:
- Multiple data sources with different formats
- Sequential steps that depend on previous results
- Parallel processing to meet time constraints
- Quality checks and human review gates
- Reliable scheduling and error recovery
Multi-agent systems address these requirements by decomposing complex workflows into specialised components.
Our Architecture
We build agent systems with clear separation of concerns.
Data Ingest Agents
Purpose-built for acquisition:
- Connect to APIs, feeds, and file systems
- Handle authentication and rate limiting
- Parse diverse formats (XML, HTML, PDF, video)
- Validate and normalise incoming data
- Track what’s been processed to avoid duplication
Processing Agents
Where AI understanding happens:
- Summarisation and extraction
- Classification and tagging
- Entity recognition and linking
- Sentiment and positioning analysis
- Translation and transformation
Verification Agents
Quality at scale:
- Cross-reference extracted data against sources
- Validate keyword relevance in context
- Check for completeness and consistency
- Flag anomalies for human review
Reporting Agents
Structured output:
- Compile processed data into formats users need
- Generate Notion pages, emails, PDFs
- Maintain audit trails back to sources
- Deliver on schedule
Orchestration
The glue that makes it work:
- Sequential and parallel execution
- Dependency management between agents
- Error handling and retry logic
- Status monitoring and alerting
- Webhook notifications for downstream systems
How We Build
1. Workflow Mapping
We document your current process: inputs, transformations, outputs, decision points, and exceptions. This becomes the blueprint for agent design.
2. Agent Design
Each step becomes one or more agents with defined responsibilities. We specify inputs, outputs, success criteria, and failure modes for each.
3. Prompt Engineering
For AI-powered agents, prompts are code. We develop, test, and version prompts as carefully as software. Prompts live in version control with clear documentation.
4. Infrastructure
Agents run on serverless infrastructure (AWS Lambda) for reliability and scale. Each agent is independently deployable and testable.
5. Orchestration
We wire agents together with explicit dependencies. Failed steps retry automatically. Successful completions trigger downstream agents. Human review gates pause for approval.
6. Monitoring
Every agent run is logged. Dashboards show pipeline health. Alerts fire on failures. You have visibility into what’s happening and when.
Capabilities
Scheduled Pipelines
Daily, hourly, or custom schedules:
- Morning data ingestion
- Overnight batch processing
- End-of-day report generation
- Weekly summaries
Event-Driven Processing
React to triggers:
- New file uploaded
- Email received
- Webhook fired
- Manual invocation
Parallel Processing
Speed through parallelism:
- Multiple data sources processed simultaneously
- Batch operations with configurable concurrency
- Results aggregation when all branches complete
Human-in-the-Loop
AI assists, humans decide:
- Review gates at critical points
- Approval workflows for sensitive outputs
- Feedback loops for quality improvement
- Override capabilities for edge cases
Technical Foundation
Our agent systems are built with:
- AWS Lambda: Serverless compute with automatic scaling
- Neo4j: Graph database for relationship-rich data
- OpenAI/Anthropic APIs: Large language models for reasoning
- Spritz Platform: Our agent orchestration backbone
- Webhook Infrastructure: Inter-agent communication
- Notion/Email Integration: Output delivery
Example: Parliamentary Intelligence Pipeline
A real multi-agent system we operate daily:
Stage 1 – Data Ingest (BIR agents)
- Camera assembly agent: Fetches XML from chamber proceedings
- Senato assembly agent: Parses AKN format from senate
- Commission agents: Scrape committee bulletins
- Ministry agents: Monitor government RSS feeds
Stage 2 – Processing (IKI agents)
- Summarisation: Generate readable summaries from raw text
- Entity extraction: Identify people, organisations, legislation
- Topic linking: Connect speeches to bills and policy areas
- Debate renaming: Create human-readable titles
Stage 3 – Verification (YARIM agents)
- Keyword matching: Fast linguistic matching
- Relevance assessment: GPT-powered context evaluation
- Sentiment scoring: Speaker and party positioning
Stage 4 – Reporting (UC agents)
- Notion publishing: Structured daily reports
- Email distribution: Formatted briefings to subscribers
- API access: Query interfaces for downstream systems
This pipeline processes both chambers, all committees, and dozens of ministry sources daily, delivering intelligence to subscribers before their morning meetings.
Who This Is For
- Organisations with complex, multi-step data workflows
- Teams drowning in manual processing
- Companies that need reliable, scheduled AI operations
- Anyone whose current AI is “just ChatGPT”
Getting Started
Multi-agent systems start with understanding your workflow. We’ll map the process, identify where agents add value, and design an architecture that scales.