Autonomy isn't the risk
It's the point

Agents work best when they can provision, read, write, and experiment without waiting on a human. Scoped credentials, zero-copy branches, and a signed record of every action, built into the bucket, make that safe.

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Generic storage makes you chooseAutonomy or safety

Autonomy, without safety

  • Every agent ships with one broad key
  • Production is one write away
  • No record of what an agent read or wrote

Safety, without autonomy

  • A human approves every consequential step
  • Staging copies built by hand, stale within a week

IronShard removes the choice.

Built for agentsSafe on real data

agent session · ironshard
$agent connect ironshard://prod
✓connected · agent-ops-01 · OAuth
✓scope · read:/datasets · write:/reports
✓branch · prod → exp-441 · 0 B
✓running · 4 workflows · egress $0.00
✓snapshot main@14:22:15Z · rollback ready
✓audit log · 1,284 events · signed

Just evaluating? An autonomous agent can create its own sandbox bucket with no account. Agent Buckets docs

01

Start

Create an account and a bucket, then connect your agent over the production MCP server.

02

Scope

Credentials per agent, down to a prefix or key, with IP and time limits.

03

Branch

Each agent gets a zero-copy branch of production. Review the diff before merging.

04

Run

$0 egress by default, with a cost-latency dial per credential.

05

Roll back

Return to any snapshot in one call.

06

Audit

Every read, write, merge and denial, in a signed log.

What agents build on IronShard

  • RAG on production data
  • Apps and internal tools
  • Training and evals
  • Multi-agent workflows
  • Data pipelines
  • Agent workspaces and artifacts

IronShard vs. the alternatives

Without IronShard
With IronShard
One shared key for every agent
Scoped credentials per agent
Bucket-level access logs
Signed audit trail, automatic
No policy at the data level
Policy checked on every request
Custom middleware for MCP
Native MCP, no middleware

Want the provider-by-provider version? Compare IronShard with S3, R2, GCS, Azure Blob, and MinIO for agent workloads.

Common questionsAbout AI agent storage

Does IronShard support the Model Context Protocol?
Yes. IronShard exposes two MCP surfaces: a public agent MCP server where agents create and operate their own buckets with no account or OAuth, and a production server for tenant-owned buckets. Storage, snapshot, branch, and audit operations are all structured MCP tools, with no custom integration work. The full tool list, endpoint, and examples are in the Agent Buckets docs →
Which AI agent frameworks work with IronShard?
IronShard is S3-compatible and works with any framework that supports S3-based storage, including LangChain, AutoGen, LlamaIndex, CrewAI, OpenAI function calling, PyTorch, TensorFlow, MLflow, HuggingFace, and Airflow. No SDK changes required.
How does IronShard log AI agent actions?
Every request made by an authenticated agent (reads, writes, queries, inferences) is automatically written to an immutable, cryptographically signed audit log. Logs are searchable, filterable, and exportable on demand. No instrumentation required on your side.
Can I set different access levels for different agents?
Yes. Each agent gets its own credentials scoped per-bucket, per-prefix, or per-key, with optional IP address and time-window restrictions. Policies are enforced at the storage layer and can be updated without redeploying your agent.
Does IronShard help with EU AI Act compliance for AI agents?
Yes. The EU AI Act requires documented data lineage, auditability of AI system inputs and outputs, and reproducibility of results. IronShard's versioning, lineage tracking, and immutable audit logs are engineered to meet those requirements for any agent operating against IronShard storage.
Do I need to migrate my existing storage?
Your tooling does not change: IronShard is S3-compatible, so existing pipelines connect with an endpoint change, and data imports over the S3 API. Once production data is hosted on IronShard, agents branch it instantly, so they work on current data while production itself stays locked.
How does IronShard handle multi-agent workflows?
Each agent operates within its own access scope. Multiple agents can run in parallel against the same datasets without interfering with each other. IronShard's branching feature allows agents to work on isolated copies of production data simultaneously, with full audit trails per branch.