Executive Summary

Teams building multi-step LLM agents and orchestration pipelines increasingly struggle to debug failures, reproduce state, control costs, and meet audit requirements because standard logs and APMs don’t expose per-step model calls, tool invocations, and evolving agent state; this is a pain felt by an estimated 2M development teams moving agents into production. The lack of deterministic, visual tooling means engineers waste time tracing through fragmented logs, missing subtle state bugs or cost anomalies. You could build a visual, step-through debugger that captures end-to-end agent traces, lets engineers step through each LLM and tool call, inspect and modify intermediate state, replay executions deterministically, and surface per-step latency and cost; delivered as a low-overhead SaaS or on-prem connector with out-of-the-box adapters for major agent frameworks and observability stacks. Include features for lineage, policy/audit logs, and CI gating so teams can enforce correctness before deploying agents. The market looks attractive now: we estimate a $6.0B addressable market (2M teams × $3K ACV) and indicators like an 88/100 market score and 82/100 revenue potential reflect strong demand driven by agentization, productionization of LLMs, and rising investment in observable AI. You can differentiate by prioritizing developer UX (source-level, replayable debugging), deep, low-overhead integrations with popular agent frameworks and observability tools, and audit/cost controls—while being honest about challenges around instrumentation complexity, data privacy, and a medium-competitive landscape that will require strong go-to-market partnerships and seamless onboarding.

Market Opportunity

Visual, step-through debugger for agent workflows and state targets a $6.0B = 2M development teams × $3K ACV for tooling and observability per team total addressable market with medium saturation and a year-over-year growth rate of 30% YoY — based on growth in AI developer tooling and ML observability demand (industry reports 2023-2024).

Key trends driving demand: Agentization of workflows — more products are orchestrating LLM calls and external tools, creating multi-step execution traces that need specialized debugging.; Shift from experimentation to production — teams are deploying LLM agents in customer-facing scenarios, increasing demand for reliability, auditing, and cost control.; Observable AI — industry focus on model observability and governance is driving investment in tooling that captures traces, metrics, and lineage for model-driven systems..

Key competitors include LangSmith, Weights & Biases (WandB), WhyLabs.

Sign in to access

Visual, step-through debugger for agent workflows and state

Executive Summary

Market Validation

Market Opportunity

More in Developer Tools

Manage dozens of websites with centralized automation and governance

Reduce latency & cost with AI-driven backend optimization for mobile games

Missed sales from phone leads fixed by an API phone system that captures and qualifies

AI coding tools lose context, provide persistent cross-tool memory

Open-ended scientific tasks lack rigorous, domain-expert benchmarks

Fix fragile delivery-app checkout flows with AI-driven test & observability