Hard part about building AI Agents isn't planning it's making them stick to plan (sia.build)

Sia.build argues the hardest part of building AI agents isn’t getting them to make plans but making them reliably follow, verify, and recover from those plans in production. After hundreds of deployments they identify three core challenges: rigorously defining an agent’s role (domain knowledge, explicit boundaries, success metrics, safety constraints), limiting and dynamically injecting tools (to avoid context bloat, decision paralysis and security attack surface), and enforcing plan execution with strict tracking and verification. Measured results are striking: clear roles yield ~40% faster completion and 70% fewer errors, tool schemas can eat 20–40% of context but dynamic injection reduces context usage by 70–80%, and their execution model moves success rates from ~58% to ~96% with 89% auto-recovery. Technically, their platform treats plans like tracked todo lists (pending → in_progress → completed/failed), restricts allowedTools per step, detects and demands justification for deviations, and verifies outcomes (e.g., file exists, API status codes, DB writes) before proceeding. Multiple guardrail layers—static analysis, permission checks, cost estimates, rate limits, circuit breakers, runtime anomaly detection, outcome verification, rollback and audit logs—prevent catastrophic failures (runaway API loops, destructive deletes, credential leaks). The implication for AI/ML teams is clear: production-grade agents require orchestration, observability, and recoverability (git-like revision history), not just larger LLMs—shifting engineering focus from model design to systems, safety, and MLOps architecture.