Antigravity Lab's Agent Playbook Tackles the Orchestration Crisis

Antigravity Lab dropped a 12,000-word patterns guide last week that reads like a post-mortem of every failed agent deployment from the past year. The timing isn't accidental. May 2026 marks roughly 18 months since ChatGPT plugins launched, and the industry has cycled through enough "autonomous agent" hype cycles to fill a graveyard.

The guide zeroes in on three patterns that determine whether an agent system scales or implodes: task decomposition granularity, sub-agent handoff structures, and loop termination controls.

The Decomposition Problem

"Most multi-agent systems start out simple," notes Microsoft's Agent Framework team in their handoff pattern documentation. A router agent receives a request, picks a specialist, and returns the result. This works until it doesn't.

Antigravity's analysis shows that decomposition failures follow a predictable pattern. Teams start with coarse-grained tasks ("analyze this codebase"), hit context limits, then overcorrect with micro-tasks that create coordination overhead exceeding the original problem's complexity.

The sweet spot, according to their production data: tasks that fit within 4,000 tokens of input context and produce under 2,000 tokens of output. Larger than that, you hit model limits. Smaller, and you're paying coordination tax.

Handoff Architecture Without the Theater

Augment Code's architecture guide strips the buzzwords from multi-agent coordination: "Multi-agent orchestration is a coordination layer that decomposes complex tasks into subtasks, routes each subtask to a specialized agent, maintains shared state across agent boundaries, and recovers from failures at every handoff point."

Three handoff patterns dominate production deployments:

Sequential handoffs work like a factory line. Agent A completes its task, passes state to Agent B. Simple to debug, terrible for parallelizable work.

Parallel dispatch sends subtasks to multiple agents simultaneously. Great for independent tasks, but state reconciliation becomes the bottleneck.

Hierarchical orchestration uses a controller agent to manage sub-agents dynamically. Most flexible, highest overhead.

Claude Lab's production patterns guide found that 73% of failed agent deployments traced back to handoff failures, specifically state corruption during parallel execution.

The Loop Control Crisis

Resilio Tech frames the core challenge: "The moment you let a model choose tools, make multi-step plans, retry or reformulate tasks, read and write state, call internal services, escalate costs across a loop, you switch from deterministic to probabilistic infrastructure."

Loop control isn't about preventing infinite loops (though that matters). It's about knowing when an agent should stop trying.

Antigravity identifies four termination triggers that actually work:

Token budget exhaustion: Hard stop at predetermined token spend. Time bounds: Wall-clock limits for real-time systems. Convergence detection: Output stability across iterations. Explicit success criteria: Measurable completion conditions.

The surprise finding: explicit success criteria failed most often. Agents would satisfy the criteria through technically correct but practically useless outputs. Token budgets, despite their crudeness, prevented more production disasters.

Implementation Reality Check

The guide's most valuable section might be its failure taxonomy. Common patterns include:

Context pollution, where agents accumulate irrelevant state across handoffs. One production system at a Fortune 500 grew its context by 500 tokens per handoff, hitting limits after just eight steps.

Orchestrator bottlenecks, where the controlling agent becomes a single point of failure. Microsoft's pattern documentation specifically warns against deep hierarchies for this reason.

Recovery theater, where retry logic creates the illusion of robustness while actually compounding errors. Antigravity found that systems with aggressive retry policies had 3x higher total failure rates than those with conservative limits.

The Production Checklist

Antigravity's guide concludes with a pre-deployment checklist that reads like hard-won wisdom:

Measure coordination overhead before adding agents. If task handoff takes more than 20% of total execution time, you have too many agents.

Implement state checkpointing at every handoff boundary. Not just for recovery, but for debugging when things inevitably break.

Set token budgets 50% below what you think you need. Every team underestimates context growth.

Build kill switches for every loop. Not just timeouts, but human-accessible abort mechanisms.

Log everything, but structure logs for agent-specific debugging. Traditional APM tools weren't built for non-deterministic, multi-step processes.

The guide's parting observation: successful agent systems look boring. They decompose predictably, handoff reliably, and terminate definitively. The exciting architectures, the ones with emergent coordination and dynamic hierarchies, are the ones that call you at 3am when they've burned through your monthly OpenAI budget trying to parse a malformed JSON file.