AI Agent Platform

Connect your data.
Deploy autonomous agents.
Sleep at night.

Mycelium connects AI agents to your databases, tools, channels, and documents through MCP — then governs every action they take. Every tool call checked. Every dollar capped. Every secret protected. Agents work autonomously. You stay in control.

See it in action

MCP-native: any database, tool, or channel — 16 curated, infinitely extensibleAgents, swarms, and graphs — governed by designZero secrets exposed to agents. Ever.

agent://research-analyst

▸ Summarize Q3 customer feedback and flag urgent issues.

Policy ✓ Budget tracked 3 tools approved

Budget$0.42 / $100

Tokens3.1k / 10k

Sources7 connected

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Agent types

single, swarm, graph — compose into teams

∞

MCP integrations

16 curated + any custom MCP server you bring

Secrets exposed

keys injected from vault — agents never see them

100%

Actions governed

every tool call, token, and delegation — checked

What becomes possible

When you can trust your agents, you let them do more.

Most teams keep agents on a short leash — limited tools, limited data, constant supervision. Mycelium changes the math. When every action is governed, you can afford to let agents do real work.

Query your database

An agent connects to Postgres, runs analysis, and reports findings to Slack. Read-only enforced. Every query policy-checked.

Research at scale

Five agents fan out across the web, GitHub, and your internal docs. Results converge into a report. Untrusted sources flagged automatically.

Run complex workflows

A graph pipeline: collect, analyze, draft, verify, publish. Versioned. Replayable. Each step under full trace.

Answer from your knowledge

Upload documents, sync Confluence and Google Drive. Hybrid retrieval with reranking. Agents answer grounded in your data — not hallucinations.

Here's the thing

Useful agents need access. Access without control is a risk.

An agent that can't reach your data is useless. An agent that can reach your data without limits is dangerous. Today, teams choose one or the other.

Your keys, in the wild

API keys passed as environment variables. One exposed log line, one misconfigured container — and your credentials are public.

1 leaked key = full access

The $5,000 retry loop

An agent stuck retrying burns through LLM tokens at scale. No budget guardrail means you find out on the invoice.

No warning. No limit.

The poisoned prompt

An agent reads a web page with a hidden instruction. Now it's emailing customers or modifying records — with your credentials, through your tools, without your knowledge.

1 injection = total compromise

The Mycelium layer

What if every dangerous thing an agent could do… simply stopped?

Mycelium sits between your agents and everything they touch. Not logging after the fact. Not alerting when it's too late. Intercepting — before the action happens — and checking it against your rules.

The principle

A running agent has exactly two ways to reach the outside world. Both pass through Mycelium. Both are checked. The agent holds no keys, no tokens, no credentials.

Gateway

Tools are gated

Every tool call — database query, Slack message, API request — passes through policy before it executes. Allowed tools only. Correct arguments only. And if the agent has touched untrusted data, dangerous actions stop until a human says yes.

Secret Proxy

Spend is capped

Every LLM call routes through the proxy. Tokens metered in real time. Budget exceeded? The proxy returns 402 — the agent stops generating, mid-sentence if it has to.

Vault

Secrets stay secret

Credentials live in an encrypted vault. They're injected at the exact moment a tool needs them — into the upstream, never into the agent. The agent doesn't know your keys exist.

Connect

Three ways to plug agents into your world.

Agents need data, humans need access, and everything needs governance. Mycelium handles all three through one unified layer.

MCP Tools

Agents reach databases, APIs, repositories, and external services through MCP — the Model Context Protocol. 16 curated templates ship with governance-safe defaults: egress allowlists, credential refs, taint and sink rules. Any custom MCP server — stdio or HTTP — connects in minutes.

PostgreSQLDatabase

Read-only queries, egress allowlist, credential injection

SQLiteDatabase

Read queries by default, writes require approval

GitHubDeveloper

Scoped tokens, create_issue gated as external_comms

GitLabDeveloper

Merge requests require approval after untrusted input

SlackCommunication

post_message gated as external_comms

GmailCommunication

send_email requires approval, read is open

Brave SearchWeb

Output marked untrusted — taints the session

Google MapsWeb

Geocoding and directions, no write surface

Google DriveStorage

Search and read, no delete or modify

Google CalendarProductivity

create-event gated as external_comms

FilesystemStorage

Directory allowlist, path validation

FetchWeb

Egress allowlist only, output untrusted

16 curated templates — plus memory, git, time, and any MCP server you bring.

Build your own

Any MCP server — stdio or HTTP — connects in minutes. Define the tools, set the policy, deploy.

Channels

Channels are how humans talk to agents. Connect a Telegram bot, a Slack workspace, or embed a chat widget on your site. Bot tokens are injected out-of-band from the vault — never exposed to the agent or stored in its environment. Every channel inherits the same governance: policy-checked, budget-capped, fully traced.

Telegram botSlack workspaceWeb chat widgetAny channel adapter

Users reach agents through channels. Agents reach systems through MCP. Both are governed.

Knowledge Base

Upload PDF, DOCX, HTML, CSV, images with OCR. Sync external sources — Confluence, Google Drive, Notion. Hybrid retrieval: dense vector + lexical, fused with reciprocal rank fusion, optional reranker. KB results are untrusted by default — consuming them taints the session.

Run

Three ways to work. One governed platform.

Every agent is one of three types. But they're not isolated — they compose into teams that tackle work no single agent could.

Single

One agent, one task.

An autonomous agent that reasons step by step, calls governed tools, and streams the answer in real time. Simple, fast, effective.

Swarm

Many agents, one goal.

A self-organizing mesh. Tasks land on a shared board and are claimed by the best-matching agent — based on capability, reputation, and load. Complex tasks auto-decompose into subtasks. Results roll back up. No central dispatcher. No single point of failure.

Graph

A workflow you design.

Build versioned workflows in the visual Graph Studio. Parallel branches, map operations, conditional edges, subgraphs. Deploy, run, replay. Each node is a governed step — LLM call, tool call, or decision.

And here's the thing

They don't just coexist. They collaborate.

Because every agent type shares the same governed task board, they compose into teams — any combination, any hierarchy.

→A single agent hits a complex problem → delegates a subtask to a swarm
→A graph node fans out to five agents → results converge at the next node
→A swarm worker hands off to a graph for structured processing
→Agents signal each other by name — coordinate without a conductor

One team. Mixed types. Shared board. Durable delegation. Every interaction governed — every subtask policy-checked, budget-capped, and traced. Just like the parent.

Govern

The six things that happen before an agent acts.

Not after. Not in parallel. Before. Every tool call, every generation, passes through these gates — in order, every time.

Allowlist + RBAC

What's allowed

Only declared tools. Only for the caller's role. The agent can't discover or invoke anything you haven't explicitly approved.

Argument validation

What's valid

Per-parameter rules: regex match, max length. The agent can't pass a DROP TABLE through a tool that expects SELECT.

Taint → Sink

What's poisoned

When the agent consumes untrusted data — web pages, KB results, external API responses — the session is tainted. A tainted session calling a dangerous sink (payment, external comms, code execution) is blocked until a human approves.

Dual-LLM quarantine

What's quarantined

Output from untrusted servers is withheld behind an opaque reference. The agent gets only structured fields — extracted by a sandboxed model. Raw, potentially injected text never reaches the agent's context.

Budget hard-stop

What it costs

Every LLM request is clamped to the remaining budget. Streaming included — the provider stops mid-generation if the budget is exhausted. The proxy returns 402. No surprise invoices.

Full audit trail

What happened

Every tool call, every generation, every policy decision — recorded with timing, tokens, cost, and outcome. Stream to the Trace Explorer, export to OTLP or Langfuse. Nothing happens in the dark.

The difference

Without governance vs. with Mycelium.

Without Mycelium

With Mycelium

Credentials

API keys in env vars — visible to the agent

Injected from vault at call time — agent never sees them

Budget

Find out on the invoice

Hard 402 at the proxy — mid-generation if needed

Dangerous actions

Agent does whatever its tools allow

Untrusted data blocks sinks until you approve

Prompt injection

Agent follows injected instructions

Untrusted output quarantined — agent gets structured fields only

Multi-agent

Sequential calls or custom glue code

Agents, swarms, and graphs compose into governed teams

Audit

Build your own logging

Every action traced, exported to OTLP or Langfuse

Isolation

Shared environment, manual separation

Per-tenant Kata sandbox, default-deny network

See it in action

See governance in action

Three agent modes, one governed platform. Toggle the trace to see every policy check, tool call, and token — accounted for in real time.

Trace

One autonomous agent: it reasons, calls governed tools and streams the answer token by token.

In practice

What teams build with Mycelium.

Governed analytics agent

Connect Postgres (read-only). Agent analyzes weekly signups, detects anomalies, posts findings to Slack. Every SQL query validated. Budget capped at $5. No write access — ever.

PostgresSlackSingle agent

Competitive research swarm

Five agents research across Brave Search, GitHub, and your KB. Tasks distribute across the mesh by capability. Results converge. Web data is tainted — the swarm can't email anyone without approval.

Brave SearchGitHubSwarmTaint protection

Documentation pipeline

A graph: collect changes → draft → verify links → publish. Each step traced. Publishing requires approval. The graph is versioned — roll back or replay any run.

Graph StudioApprovalsVersioned

Mixed-team product launch

A graph coordinates the pipeline. One node dispatches a swarm to research the market. Another triggers a single agent to pull historical metrics from Postgres. Results converge — the graph compiles the launch report. Three agent types, one team, every step governed.

TeamGraph + Swarm + SingleFull audit

Enterprise-ready

Built for production from day one.

Deploy your way — self-hosted, managed cloud, or on-premise. With the isolation, identity, and supply-chain integrity a business needs.

Self-hostedManaged cloudOn-premise / VPCSSO & RBAC

Per-tenant isolation

Namespace per tenant, Kata-sandboxed pods, default-deny NetworkPolicy, region pinning for data residency.

Identity & access

OIDC SSO, RBAC capability model, per-session HMAC tokens with epoch fencing, brute-force protection.

Runs in your environment

Docker Compose, Kubernetes operator, or managed cloud. Your data stays where it belongs.

Supply chain integrity

SBOM + SLSA provenance, cosign-signed images, Trivy + gitleaks in CI, digest-pinned base images.

Warm pool

Pre-started agent servers for zero-cold-start session claims. Scale-to-zero when idle.

Let your agents do real work.

Connect a data source. Deploy an agent. Watch every action get policy-checked — in minutes, not weeks.

Read the docs

Connect your data.Deploy autonomous agents.Sleep at night.