A powerful model is potential, not productivity. harness.ms is the orchestration layer that wraps AI agents in structure — defining tasks, routing steps, validating output, and recovering from failure — so AI does real work you can trust.
harness.ms wraps models and agents in structure — so AI does real work reliably, observably, and at scale.
Define multi-step workflows across models and tools with clear inputs and outputs at every step. Route each task to the best-fit agent and recover gracefully from any failure.
Evaluate agent behavior against realistic scenarios before it touches production. Catch regressions early, replay any run step by step, and track cost and latency per workflow.
Complete action tracing, scoped agent permissions, and architecture that scales from a single task to a cooperating fleet — integrated with the infrastructure you already run.
Four capability layers that turn impressive demos into dependable production systems.
Define sequences across any combination of models, tools, and APIs with explicit inputs, outputs, and validation rules at each step.
Automatically route each step to the model or agent most suited to it — balancing capability, cost, and latency per task.
Configurable retry policies, fallback agents, and error recovery so one timeout or malformed output doesn't sink the entire run.
Insert human-in-the-loop gates at exactly the workflow steps where judgment, compliance, or sign-off is required.
Measure agent behavior against a library of realistic production cases before any run touches live systems or real data.
Automated quality comparisons across model versions and workflow changes — catch drops before users experience them.
Reproduce any historical run step by step with the exact inputs, tool calls, and model responses that produced the original output.
Track cost, latency, token usage, and reliability per workflow — surfaced in dashboards that connect AI spend to business outcomes.
Every action, decision, tool call, and model response is logged in a structured, searchable trace — for debugging and compliance alike.
Drill into any individual step in a run: the input it received, the output it returned, the time it took, and any errors it triggered.
Live visibility into workflow health, agent performance, failure rates, and cost — across every environment from dev to production.
Configurable alerts on failure rates, latency spikes, and cost thresholds — routed to Slack, PagerDuty, or your existing SIEM.
Each agent operates inside explicit, least-privilege guardrails — preventing any single agent from accessing resources outside its defined scope.
Tamper-proof logs of every agent action for regulatory compliance, internal audit, and incident investigation in regulated environments.
Org-level policies enforce what agents can call, when human approval is required, and what outputs are permissible — applied consistently fleet-wide.
Agents access tools and APIs through managed credential injection — no plaintext secrets in agent context, no credential sprawl.
Four steps that transform raw model capability into production reliability.
Specify the steps, the tools, where humans approve, and the output you expect.
harness.ms routes each step to the right agent and validates the result before proceeding.
Failures trigger retries, fallbacks, or escalation — never silent errors or crashed runs.
Every run is fully logged, measurable, and replayable for debugging and compliance.
harness.ms begins with a workflow definition — the steps, the tools each step can call, the models best suited to each task, where humans must approve, and what a valid output looks like. Structure defined upfront is control applied everywhere.
Most agent failures aren't model failures — they're infrastructure failures. harness.ms adds the retry logic, validation, approval gates, and observability that production demands.
Full traceability of every decision and action means your team, your compliance team, and your customers can trust what the agent did — and why.
Scoped permissions and policy enforcement keep guardrails in place whether you're running one agent or a fleet of cooperating ones across departments.
Scenario evaluation and regression detection surface quality drops during development — not after a degraded experience reaches your customers.
harness.ms fits the infrastructure and tools you already run. No rearchitect, no vendor lock-in, no forcing functions on your model choices.
The organizations extracting real value from AI are the ones that wrapped it in structure. harness.ms is that structure — turning impressive into dependable.
From prototype to production, regulated to high-velocity — harness.ms adapts to the environments where AI reliability actually matters.
Move agent projects from sandbox to production without rebuilding from scratch. Wrap your existing agents in harness.ms structure and ship with confidence.
Orchestrate agents that span APIs, databases, code execution, and external services — with validation and fallback logic at every integration point.
Deploy AI in finance, healthcare, and legal contexts where every agent action must be traceable, auditable, and defensible under compliance scrutiny.
Build workflows where AI handles the volume and humans handle the judgment — with approval gates that are native to the orchestration layer, not bolted on.
Manage dozens of cooperating agents across departments or business units — with consistent governance, shared policies, and unified observability.
Give engineering teams the evaluation infrastructure to iterate on agents quickly — without accumulating reliability debt that bites in production.
We had a great research agent — until it hit an edge case in production and silently returned garbage. harness.ms gave us the validation layer, retry logic, and full trace that turned it into something we could actually depend on.
The human-in-the-loop approval gates were the dealbreaker for our legal team. With harness.ms, we could show exactly where a human reviewed the output before any action was taken. That's what got us into production.
We ran our agent fleet for three months before deploying to customers. harness.ms's scenario evaluation caught five regressions that would have been production incidents. The replay feature alone was worth the investment.
Everything you need to know about getting your AI agents into production reliably.
Models keep getting better, but capability was never the blocker — reliability was. harness.ms is the structure that turns impressive AI into dependable outcomes. Get your agents into production.