Prologue
The Pain
2 weeks deploying agents blind. Silent failures. $40/hr burn. Duct-tape observability.
Ch 1–2
5 Ideas → Kill
5 candidates generated. Picked FormsFlow. Built prototype. "Lame." Killed it.
Ch 3–4
The Revelation
"WOW. That's it." Observability was the pain all along. Vision crystallizes.
Ch 5–6
Specs + Build
Full-day specs sprint. 2 teams, parallel build. ~2hrs coding total.
Ch 7–8
Audit Machine
450+ checkpoints. 12 critical contract mismatches caught. Cross-team review every phase.
Ch 9–11
Real Data → Redesign
"I see data but I don't get it." Full UI/UX redesign. Only 3 new endpoints needed.
Ch 12
Ship It
Feb 12, midnight. Running platform. Real agents monitored.
What HiveBoard Does
Agent-level observability for production AI systems
1
Agents as workers — not LLM calls, not traces. Tasks, heartbeats, stuck states, recovery paths.
2
Invisible failures — sees what agents didn't do. Queue rot, dropped tasks, silent misses.
3
One event stream — single data primitive. Status computed, not stored. No stale state.
4
Framework-agnostic — LangChain, CrewAI, AutoGen, custom. HiveBoard doesn't care.
Developer Experience
3 lines → heartbeat. Decorators → timelines. Events → full story.
import hiveloop
hb = hiveloop.init(api_key="hb_xxx")
agent = hb.agent("lead-qualifier")
# Agent appears on dashboard. Done.
Layer 0 — Presence
3 lines, heartbeat + stuck detection
Layer 1 — Timelines
@decorators on existing functions
Layer 2 — Full story
LLM costs, plans, issues, queues
The Gap HiveBoard Fills
| Capability |
LangSmith |
Langfuse |
Datadog |
HiveBoard |
| Agent heartbeat |
— |
— |
— |
✓ |
| Stuck detection |
— |
— |
— |
✓ |
| Task timelines |
✓ |
— |
— |
✓ |
| Intent pipeline |
— |
— |
— |
✓ |
| LLM cost tracking |
✓ |
✓ |
— |
✓ |
| Framework-agnostic |
— |
✓ |
✓ |
✓ |
| Agent-as-worker model |
— |
— |
— |
✓ |
Feature comparison based on publicly documented capabilities as of Feb 2025.
"3 lines of code. 30 seconds. Your agent has a heartbeat."
"The most dangerous agent failure is the one that doesn't look like a failure."
"Your agents are working. Are they healthy?"