aiagents

Artificial intelligence is moving from systems that answer questions to agents that can actually perform tasks. But giving AI the ability to act creates a difficult challenge: How do we make autonomous agents powerful without making them dangerous?
@NEAR Protocol is approaching this problem from an infrastructure perspective. Its latest release, #IronClaw 1.0, introduces an architecture designed to separate an AI agent's decision-making from its ability to take action, creating a safer foundation for autonomous AI.
IronClaw 1.0: Separating Thinking from Acting
Traditional #AIAgents can have a model directly connected to tools, credentials, websites and other external systems. This creates a single point where reasoning and execution become tightly connected.
IronClaw takes a different approach.
Its architecture separates the component that decides what should happen from the component that actually performs the action. Between them sits a coordination and security layer called the Guard.
Every action passes through this layer, allowing IronClaw to enforce security policies consistently. Sensitive operations require explicit approval, while credentials and secrets are handled with additional protections.
This architecture means an agent can reason about what needs to be done without automatically receiving unrestricted power to execute every decision.
Benchmark Performance That Stands Out
The architecture is backed by measurable results.
Using the deepseek-v4-flash base model, IronClaw 1.0 achieved leading results across three different agent benchmarks:
PinchBench: 93.5%
Testing real-world tasks such as scheduling, email management, coding, research and file operations.ClawBench: 88.6%
Testing agents across more than 140 real production websites and complex, multi-step tasks.OfficeQA: 76.4%
Testing grounded reasoning across a massive collection of enterprise documents.
These results are significant because they demonstrate the performance of the agent harness alongside the underlying model. IronClaw is not simply about having an intelligent model; it is about building the infrastructure that allows intelligence to become useful action.
Designed for Real, Continuous Work
IronClaw's strengths extend beyond benchmark scores.
Safer by design: Sensitive actions can require explicit approval, while secrets can be isolated and scrubbed from logs.Persistent state: IronClaw continuously checkpoints its work. If an agent is interrupted, restarted or paused while waiting for permission, it can resume instead of starting from zero.Omni-channel memory: The same assistant can operate across the CLI, web, Slack and Telegram while maintaining consistent memory and safety rules.Team isolation: Different users and teams can operate within isolated environments, helping separate credentials, workspaces and responsibilities.
Together, these features address an important problem with autonomous AI: reliability is about more than producing the right answer. An agent needs to remember what it was doing, operate safely, and continue working across the environments where people actually communicate.
NEAR AI and the Role of Staking
IronClaw is part of a broader NEAR AI vision focused on user-owned and verifiable AI. #NEARAI provides infrastructure for private inference and secure AI agents, including hardware-backed Trusted Execution Environments designed to keep sensitive data and computation isolated.
But secure AI also needs secure decentralized infrastructure underneath it.
That is where NEAR staking becomes important.
NEAR uses Proof-of-Stake, where users delegate NEAR to validators that participate in securing the network and reaching consensus. Staked NEAR therefore represents economic security behind the decentralized infrastructure that processes transactions and supports applications.
So staking should not be viewed only through the lens of yield.
As AI agents increasingly interact with blockchains, assets and applications, the infrastructure they depend on must remain decentralized, reliable and economically secured. NEAR's roadmap explicitly positions NEAR AI and the blockchain layer as complementary pieces of an emerging agent economy.
IronClaw shows how AI agents can become safer and more capable. NEAR AI provides the secure AI infrastructure, while NEAR's decentralized network and staking provide an economic security layer underneath it.
The bigger vision is clear: AI agents that can think, act and transact on behalf of users, while users retain greater ownership, privacy and control.