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chronoly — agent trace

$ claude -p 'Add auth to my API'

→ Chronoly Blackbox: tracing enabled

● Read src/server.ts

● Read src/routes/api.ts

● Write src/middleware/auth.ts

⚠ Agent Shield: blocked rm -rf node_modules

● Bash(npm install jsonwebtoken)

◉ Cost Guard: $0.07 | 3 turns | no loops

✓ Auth added. 0 safety violations. Trace saved.

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The Problem

AI agents are powerful in demos. Messy in real work.

68% of deployed agent systems need human intervention within 10 steps. 74% rely on manual evaluation. The problems are structural, not model-level.

psychology_alt73%

Constraint Decay

Long sessions lose context. Important instructions get compacted away. Your agent forgets what it was told and repeats itself.

of agents repeat actions

warning41%

Safety Evaporation

Prompt-layer safety controls aren't real boundaries. Agents find alternative paths to execute denied actions through other tools.

bypass prompt safety

memory2.3x

Memory Contamination

Persistent memory acts like an ungoverned database. Preferences bleed across users. Shared sessions leak context between projects.

more errors with memory

loop$47

Infinite Loops

Expired tokens, failed tool calls, and context overflow trigger endless retry loops that burn tokens and crash sessions.

avg wasted per loop

search_off68%

Lost in the Codebase

Agents follow imports indefinitely, burning timeout on exploration instead of converging on a plan. The #1 failure mode in large repos.

need human intervention

bug_report74%

Blind Debugging

Dashboards show that a run failed. They can't tell you which step, which agent, or what missing state caused it.

rely on manual eval

How It Works

link

Connect

Drop Chronoly into your existing workflow. One env var. Works with Claude Code, Cursor, Hermes, and any OpenAI-compatible agent.

visibility

Observe

Every agent action gets recorded, classified, and scored. See what's happening in real time on a live dashboard.

tune

Control

Set budgets, enforce safety rules, catch loops before they burn tokens. Intervene automatically, not manually.

of agent runs need human fix

more tokens burned in loops

0min

avg wasted per failed run

Zero

tools that solve this (until now)

What We're Building

The Control Layer

Three tools that give you visibility, safety, and cost control over AI agent workflows.

videocam

Agent Blackbox

See everything your agent does

Record every agent run: prompts, tool calls, file changes, commands, errors, and decisions. Full replay-grade traces, not just chat history.

▸Full prompt & response logging
▸Tool call chain visualization
▸File diff tracking with rollback
▸Timeline replay of entire sessions

TRACE DEPTHFULL

shield

Agent Shield

Real boundaries, not prompt suggestions

Real safety checks before agents edit files, run commands, or call APIs. Enforced at the OS and network level, not just in prompts.

▸OS-level file write guard
▸Network request filtering
▸Command execution sandbox
▸Per-tool permission policies

ENFORCEMENTOS-LEVEL

speed

Cost Guard

Stop burning money on broken loops

Detect loops, wasted retries, and runaway token usage before they become expensive. Know your cost per successful task, not just total spend.

▸Real-time loop detection
▸Token budget per task
▸Cost-per-success metrics
▸Automatic circuit breaker

DETECTIONREAL-TIME

Built For

For builders who use AI agents in real projects

If your agent has ever broken code, lost context, repeated itself, burned tokens, or made a risky change — Chronoly is for you.

Vibe coders running agents daily

Indie hackers automating builds

AI agent builders shipping tools

Dev teams using Cursor, Claude Code, or Hermes

Founders automating business workflows

Join the Waitlist

We're currently talking to builders who use AI agents in real projects.

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