managed-agent

A command for asking OloLand's hosted deal specialists to analyze a specific transaction. A deal specialist focuses on areas such as risk, financial quality, screening, financing, or an investment-committee memo.

In plain words
What is it for?
Use it for risk mapping, forensic quality-of-earnings analysis, pre-LOI screening, financing preparation, lender-package preparation, or an investment-committee memo.
Why use it?
It sends questions that need a dedicated, source-based deal review to specialists that work through OloLand's controlled data access.

Command

Part of the ololand-dd plugin — 47 commands, 3 agents shipped together

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add commands/ololand-ai/ololand-plugins/managed-agent
Clone the repo
git clone --depth 1 https://github.com/ololand-ai/ololand-plugins

Or install ololand-dd, the plugin that ships this one along with the rest of its 47 commands, 3 agents.

Per session 114 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,393 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00114 $0.01393
Opus 5 $0.00057 $0.00696
Sonnet 5 $0.00023 $0.00279
Haiku 4.5 $0.00011 $0.00139

Measured 2d ago against content hash fb00737b771f, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

managed-agent scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/ololand-dd/commands/managed-agent.md · 109 lines

How it starts

The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/managed-agent (cloud deal specialists)

Runs one of OloLand's managed deal specialists — agents hosted on the Claude Platform that work a deal through OloLand's scoped, audited tool rail and return a sourced, citation-bearing answer. Use it when a question deserves a dedicated specialist pass rather than an inline answer: a full risk read, a forensic QoE battery, a pre-LOI screen, financing prep, or an IC-grade synthesis where the coordinator delegates to risk, forensic, and memo specialists in parallel.

Usage

/managed-agent <deal_id> <specialist> <question or instruction>
/managed-agent list

Arguments

  • deal_id (required) — the OloLand deal the specialist should work on.
  • specialist (required) — one of the template_ids returned by mcp__ololand__list_managed_agents. Typical roster:
    • risk-chat — maps findings to the risk taxonomy with severity, likelihood, evidence
    • forensic-chat — Beneish, Benford, EBITDA bridge, cross-document reconciliation
    • screen-chat — pre-LOI go/no-go: premium analysis, accretion/dilution, antitrust HHI
    • financing-chat — financing analysis, capital-provider sourcing prep, lender-package prep
    • ic-memo — IC memo drafting; can persist a DRAFT IC package
    • ic-coordinator — multi-agent IC synthesis (delegates to risk + forensic + memo in parallel)
    • lead-orchestrator — general sourced deal Q&A
  • question (required) — what the specialist should do.

Execution

This is an asynchronous managed-agent run. Do not expect an immediate answer; start it, then poll.

  1. list mode. If the user passed list (or asked what specialists exist), call mcp__ololand__list_managed_agents and render each agent's template_id, description, and whether it is configured in this environment. Recommend the best fit for what the user is working on, then stop.
  2. Start. Call mcp__ololand__run_managed_agent with deal_id, template_id, and prompt. It returns a task_id (with task_type: "managed_interactive_agent").
    • {"error_code": "not_found"} — wrong deal id; confirm it with mcp__ololand__list_deals.
    • {"error_code": "invalid_template"} — the specialist name is not callable; show the roster from mcp__ololand__list_managed_agents and let the user pick.
    • {"error_code": "not_configured"} — the specialist exists but is not provisioned in this environment; relay that and suggest another specialist from the list output that shows configured: true.
    • {"error": "Company scope required …"} — the MCP connection is not company-scoped; tell the user to reconnect / sign in, do not retry blindly.
    • Any other tool-level error fails closed — relay it rather than synthesizing an answer.
  3. Poll. Call mcp__ololand__check_task_status with the task_id every few seconds. While state is STARTED/PROGRESS, relay the progress message. The IC coordinator's delegating turns run multi-minute — keep the user informed rather than giving up. Keep polling until state is SUCCESS or FAILURE.
  4. On FAILURE — report the error verbatim. Do not fabricate a specialist answer.
  5. On SUCCESS — the result object is:
    • response — the specialist's answer, already source-cited. This is the deliverable; render it unaltered.
    • run_id — the OloLand agent_runs audit row.
    • claude_platform_session_id — the hosted session for operator trace.
    • tool_calls, input_tokens, output_tokens — run telemetry.
    • citations — structured citation payloads backing [N] markers, when present.

Read the full file on GitHub · 109 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 109 lines · 114 tokens per session scan A fb00737b771f

Subscribe to this mod's changes

managed-agent is a command published in the GitHub repository ololand-ai/ololand-plugins (0 stars, last pushed 5d ago), licensed Apache-2.0. It adds 114 tokens to every session and 1,393 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.