AI Agents: Tools and Solutions

Recommendation

Start without an agent.

Use a single model call, retrieval step, AI SDK tool loop, or deterministic job until the model must choose and revise actions from tool feedback and that autonomy wins on representative evaluations.¹ ²

For: Application teams considering model-directed multi-step tool use after a simpler model call, retrieval step, or deterministic workflow fails measured requirements

Main trade-off

Agents can adapt through uncertain multi-step work while adding nondeterministic paths, repeated model and tool calls, persistent state, a larger action surface, harder evaluation, higher cost, and recovery obligations.¹ ² ³ ⁴ ⁵

Why there is no single default: Most generation, retrieval, classification, and fixed automation do not need autonomous control. After the no-agent gate passes, runtime choice changes with language, state graph, provider commitment, durability, tools, approvals, and operating ownership.

Define the workload and operating boundary

Use representative inputs, explicit acceptance criteria, current first-party facts, and a migration boundary before selecting a product.

Agent necessity
Prove a model-directed tool loop outperforms a single call or predefined workflow on representative outcomes.¹ ³
Control model
Choose a compact provider loop, explicit state graph, role-based collaboration, or integrated TypeScript runtime from the workflow rather than branding.¹
State and durability
Separate conversation history, run checkpoints, and long-term memory; define tenancy, storage, retention, deletion, migration, and recovery.² ⁴
Actions and operations
Bound tool authorization, validation, idempotency, sandboxing, approvals, traces, evaluations, turn limits, latency, retry, and spend.¹

Bounded routes

Each route belongs in the evaluation only when its model, integration, policy, and operating boundary fits the named workload.

OpenAI agent runtime

Evaluate OpenAI agent runtime

Choose OpenAI Agents SDK when OpenAI is deliberate and a compact provider-aligned runtime for tools, handoffs, sessions, approvals, guardrails, and tracing fits.

Verify: Verify provider coupling, durable session and run recovery, tool authorization, sandboxing, approval lifecycle, trace data, limits, and model or tool cost.¹

Stateful agent graph

Evaluate Stateful agent graph

Choose LangGraph when long-running work needs explicit state transitions, durable checkpoints, mixed deterministic and agentic nodes, and fine-grained interruption.

Verify: The team must own graph design, persistence, idempotent side effects, state schema migration, deployment, observability, and framework complexity.²

Multi-agent Python

Evaluate Multi-agent Python

Choose CrewAI when a Python team has measured evidence that specialized role collaboration improves an otherwise bounded workflow.

Verify: Verify whether multiple roles beat a simpler agent, plus checkpointing, tools, approvals, tracing, hosted or self-managed ownership, latency, and token cost.³

TypeScript agent framework

Evaluate TypeScript agent framework

Choose Mastra when a TypeScript team intentionally wants agents, workflows, memory, storage, and observability in one application framework.

Verify: Verify stable versus changing surfaces, persistence, memory tenancy, snapshot lifecycle, tracing, provider fidelity, deployment, and hosted-service cost.⁴