AI agent storage: MCP-compatible

Autonomy isn't the risk
It's the point

Agents do their best work 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 are what make that safe to grant. IronShard builds them into the bucket.

Create a BucketTalk to Us

Building with agents? The endpoint, tools, and examples are in the Agent Buckets docs.

AI Agent
READ /contracts/vendor_xyz.pdf
QUERY /datasets/customers/*
WRITE /reports/inference_log.json
INFER /models/llm-v3/weights
READ /datasets/training/batch_88
IronShard
✓ authenticated · agent-legal-03
✗ blocked · policy: no wildcard reads
✓ approved · WRITE
✓ approved · INFER
✓ logged · 14:22:15.230Z
Audit Log
READ · contracts · ✓ ok
QUERY · customers · ✗ blocked
WRITE · reports · ✓ ok
INFER · models · ✓ ok
sha256: a3f9c1d... · signed
// the problem

Generic storage makes you choose: autonomy or safety

Agent Activity MonitorAgent ActivityAccess LogsPolicy RulesAudit ExportSearch agents...All AgentsLast 24h6 agents · 0 actions loggedAGENTLAST ACTIONFILES ACCESSEDDATA ACCESSEDSTATUSagent-012h ago— — —— — —UNKNOWNagent-02NEVER— — —— — —UNKNOWNagent-03— — —— — —— — —UNKNOWNagent-0414 min ago— — —— — —UNKNOWNagent-05— — —— — —— — —UNKNOWN⚠ No audit data available. Agent actions are not being logged.
01

One key, every agent

Scoping credentials by hand is IAM work nobody has time for, so every agent ships with one broad key.

02

Production is one write away

Agents read and write the same buckets your team protects. Risk overwrites and deletions, or hand-build staging copies that are stale within a week.

03

Agents at approval speed

Without enforcement at the storage layer, every consequential step needs a human watching.

04

"What did it just do?"

When a run goes wrong, there is no record of what the agent read or wrote. Debugging is guesswork, results can't be reproduced.

// the solution

Built for agentsSafe on real data

One agent session, end to end: Your agent provisions its own bucket over MCP. Or hands-on from the start with your own S3 tools.

agent session · ironshard
$agent connect ironshard://prod
✓connected · agent-ops-01 · no OAuth
✓scope · read:/datasets · write:/reports
✓branch · mirror/prod → exp-441 · 0 B
✓running · 4 workflows · egress $0.00
✓snapshot main@14:22:15Z · rollback ready
✓audit log · 1,284 events · signed

Full tool list and examples in Agent Buckets.

01

Connect without an account

One create_bucket call to the public Agent MCP server and an agent has its own bucket with scoped credentials.

no OAuth · no human approval
02

Scope every credential

Fine-grained credentials per agent, with IP address and time-window restrictions.

per-bucket · per-prefix · per-key
03

Branch production, zero-copy

Mirror keeps a live fork of production. create_branch gives every agent its own writable branch with zero copying; diff_branch shows exactly what changed.

live mirror · diff before merge
04

Run at $0 egress by default

AI-optimised workspaces with a cost-latency dial per credential: read-heavy agents stay zero-egress, latency-sensitive ones get geo-optimal placement.

geo-optimal placement · no surprise bills
05

Roll back in one call

create_snapshot captures the exact state of main at a point in time; restore_snapshot returns to it in one call.

snapshots · point-in-time
06

Audit everything, signed

Writes, reads, merges, and policy denials all land in the audit log. get_audit_log returns the events.

policy denials logged · explainable
// data safety by design
// 01AGENTREQUESTRequestAgent initiatesa data action// 02AUTHEN-TICATEAuthenticatePer-agent credentialsverified instantly// 03POLICYCHECKPolicyApproved orblocked✗ blocked · rejected// 04LOGEVENTLogImmutable recordwritten & signed// 05DATASERVEDServeApproved datareturned to agent
// use cases

What agents build on IronShard

RAG on production data

Index a zero-copy branch of the live bucket. Reads cost $0 egress, and production never serves a query.

Apps your agent builds

One prompt gives the app its own bucket, scoped credentials included. No console, no IAM, no SDK code.

Training and evals

Train against a pinned snapshot. Every run is reproducible from its lineage, dataset state included.

Multi-agent fleets

Parallel agents, per-prefix credentials, separate branches. A bad run stays inside its own scope.

// comparison

IronShard vs. the alternatives

General-purpose S3 and DIY governance both leave the same gaps when AI agents are involved.

Without IronShard
✕
Shared credentials for all agents
no per-agent access scope
✕
Basic bucket-level access logs
not agent-aware · not signed · not structured
✕
No data-level policy enforcement
agents can reach anything they can reach
✕
Custom middleware required for MCP
engineering time · fragile · maintenance burden
vs
With IronShard
✓
Scoped credentials per agent
enforced at storage layer · update instantly
✓
Immutable audit trail, automatic
sha256 signed · exportable · no instrumentation
✓
Data-level policies on every request
agents only see what they're authorised to see
✓
Native MCP support, no middleware
agents connect in minutes · no custom code

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

// FAQ

Common questions about AI agent storage

QDoes 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 →
QWhich 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.
QHow 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.
QCan 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.
QDoes 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 provide this automatically for any agent operating against IronShard storage.
QDo 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, Mirror keeps a live fork of it, so agents work on current data while production itself stays locked.
QHow 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.
Works with:
LangChainAutoGenLlamaIndexCrewAIOpenAIPyTorchTensorFlowMLflowHuggingFaceAirflowAny S3-compatible workflow