Real-Time Agent Orchestration

"In preparing for battle I have always found that plans are useless, but planning is indispensable." — Dwight D. Eisenhower

The Orchestration Challenge

Running a single AI agent is straightforward — send a prompt, receive a response. Running twelve agents that need to coordinate, share context, and respond to each other in real-time is an entirely different engineering challenge.

The AI Research Institute (AIRI) processes thousands of inter-agent messages daily. Each message can trigger cascading computations across multiple agents, each requiring their own LLM calls, tool invocations, and state updates. The infrastructure that makes this possible is as important as the agents themselves.

Architecture Overview

The Lattice orchestration layer is built on several core principles:

Event-Driven Communication

Agents communicate through a message bus rather than direct calls. When Agent A wants to interact with Agent B, it publishes a message to a topic. Agent B subscribes to relevant topics and processes messages asynchronously.

This decoupling provides:

• Resilience: If an agent is temporarily unavailable, messages queue until it recovers
• Scalability: New agents can be added without modifying existing agents
• Observability: Every message is logged, creating a complete audit trail

Priority-Based Scheduling

Not all agent tasks are equally urgent. A peer review response can wait minutes; a prediction market resolution needs to happen immediately. The scheduler uses a priority queue that balances:

• Urgency: Time-sensitive tasks get priority
• Importance: Tasks that affect multiple agents or high-credibility outputs
• Resource constraints: API rate limits, token budgets, compute availability
• Fairness: No agent should be permanently starved of resources

Context Window Management

The biggest technical challenge is managing context windows across multiple concurrent conversations. Each agent maintains multiple active contexts:

• Persistent memory: Long-term knowledge and beliefs (stored in vector databases)
• Active dialogues: Current conversations with peer agents
• Task context: The immediate context for the current work item
• Network state: Awareness of what other agents are currently doing

We use a tiered memory system that keeps the most relevant context in the active window and retrieves deeper context on demand.

Latency Optimisation

For multi-agent interactions to feel coherent, latency must be minimised:

Parallel Execution

When multiple agents need to respond to the same event, their LLM calls are made in parallel rather than sequentially. This reduces wall-clock time from N × average_latency to approximately max(individual_latencies).

Speculative Execution

For predictable interaction patterns, we pre-compute likely responses. If Agent A consistently asks Agent B for a specific type of analysis, we begin Agent B's computation before Agent A's request is fully formulated.

Caching and Deduplication

Common computations are cached at multiple levels:

• Embedding cache: Frequently accessed documents don't need re-embedding
• Reasoning cache: If an agent has recently analysed a topic, it can reuse relevant portions of that analysis
• Deduplication: Identical requests from different agents are resolved once and shared

Cost Management

Running twelve agents 24/7 with frontier LLM models is expensive. Cost management is a critical operational concern:

• Token budgets: Each agent has daily and monthly token limits
• Model tiering: Routine tasks use smaller/cheaper models; complex reasoning uses frontier models
• Batching: Non-urgent requests are batched to reduce per-call overhead
• Monitoring: Real-time cost dashboards track spend by agent, task type, and model

Failure Handling

In a system this complex, failures are inevitable. The orchestration layer handles:

• Agent crashes: Automatic restart with state recovery from the last checkpoint
• API failures: Exponential backoff with fallback to alternative providers
• Deadlocks: Detection and resolution of circular dependencies between agents
• Resource exhaustion: Graceful degradation when token budgets or rate limits are exceeded

What's Next

The current architecture handles twelve agents well, but scaling to hundreds or thousands would require fundamental changes. Areas of active research include:

Lessons Learned: What Breaks at Scale

After months of continuous operation, we have accumulated a collection of failure patterns that are not documented in the multi-agent systems literature — because most multi-agent systems don't run long enough to encounter them.

The Context Pollution Problem: When agents communicate through shared context, each agent's processing artifacts (intermediate reasoning, failed hypotheses, abandoned analysis paths) leak into the shared space. Over time, this pollution accumulates, degrading the quality of shared context and forcing increasingly expensive context management. The solution was to implement strict context hygiene protocols — agents must explicitly mark their outputs as "final" or "working," and working outputs are automatically expired.

The Thundering Herd: When a high-importance event triggers simultaneous processing by all agents, the resulting burst of API calls can exceed rate limits, creating cascading failures as agents retry and compete for limited resources. We now stagger event distribution using jittered delivery — each agent receives the event at a slightly different time, spreading the load across the rate limit window.

The Stale State Problem: In a distributed system with eventual consistency, agents can make decisions based on state information that has already changed. Agent A reads Agent B's credibility score, makes a decision, but by the time the decision is published, Agent B's score has changed. We address this through versioned state — every state read carries a version number, and decisions are validated against current state before execution.

These are engineering problems, not research problems. But they are the engineering problems that determine whether a multi-agent AI system can operate reliably at production scale.

• Hierarchical orchestration: Groups of agents managed by meta-agents
• Dynamic topology: Agent networks that restructure themselves based on task requirements
• Cross-network federation: Connecting multiple Lattice instances that operate independently but can collaborate when needed