I can't tell what my agents did last week, what observability do I need?

TLDR: Dashboards show counts. They don't show what the agent saw, why it picked a tool, or where it went off the rails. Real observability is full-trajectory capture, queryable across sessions, replayable per step.

Hivemind captures every agent's full trajectory. "Show me every session where the agent called X tool with empty args" is a one-line query.

What agent observability actually means

Agent observability: Full trajectory capture (state, tools, returns, model responses), queryable by session, agent, step, or content; replayable.

Without it, post-mortems are guesswork. Costs creep up; quality drifts; you only notice when users complain.

What this requires

Key properties:

• Full state per step: Inputs, scratchpad, tool returns, model output.
• Queryable: By session, agent, time, content.
• Replayable: From any checkpoint.
• Audit trail: Append-only, signed.
• Cross-agent: One workspace covers the whole fleet.

Approaches teams try

What each gets you:

Reference architecture

Capture once; query anytime.

Agents in production
     │
     │ per-step writes
     ▼
 Hivemind workspace (queryable)
     │
     ├─► "sessions where tool X failed"
     ├─► "replay session N from step 7"
     └─► snapshot ─► training corpus

Audit, debug, and training all read the same store.

Set it up

A few commands.

1. Install

curl -fsSL https://deeplake.ai/install.sh | sh

2. Capture in production

hivemind capture --workspace prod-agents

3. Query last week

hivemind query --workspace prod-agents --since '7d'

Where this usually breaks

• Logs only: No state; no replay; no cross-session search.
• Dashboards only: Counts and aggregates miss everything that matters.
• Trace tool, separate from training: Lessons don't feed back.
• Per-agent logs: No cross-agent search.

FAQ

Works with any framework?

Yes; capture wrapper is framework-agnostic.

PII?

Per-workspace ACLs and field-level redaction.

Retention?

Configurable; snapshots can be archived.

Replay determinism?

Tool returns frozen with the run.

Cost?

Sub-millisecond per step; dominated by model calls.

Open source?

Free tier; Deeplake is OSS.

Citations

• Deeplake Hivemind, shared memory for agents.
• Anthropic. Model Context Protocol specification.
• Activeloop. Deeplake on GitHub.

What did your agents do? One query away.

Hivemind captures full trajectories, queryable across sessions and replayable per step.

Install Hivemind