Multi-agent orchestration
Flows as a graph, with retries, recovery from the last step and a specialized agent for each part of the work.
Service · AI Harness Development
The harness that takes your agents to production
We build everything around your AI agents: business memory, orchestration, approval by role, evals and cost control. Yours, auditable and ready to scale.
AI HARNESS DEVELOPMENTLIVE
What you get
Orchestrationflows as a graph
Memorycompany brain
Approvalsign-off by role
Qualitycontinuous evals
Spendcaps per flow
The problem
Your agents work in the demo, not in operations. What is missing is rarely the model. It is everything around it.
PilotIt never reaches production
It works with clean data and one user, then breaks on real-world exceptions.
Who?Nobody approves anything
The agent sends clients things no one with authority ever reviewed.
Why?Errors nobody can explain
Without traces, no one knows what the agent did, with which data or at what cost.
ZeroEvery agent starts from scratch
Each project builds its own memory, rules and connections, and nothing is shared.
What we build
Every piece an agent needs in production. On our proven harness, adapted to your processes, roles and rules.
Company brain
One memory shared by every agent: clients, cases, rules and decisions. It lives in your infrastructure and belongs to you.
Human approval by role
Checkpoints where the right person signs off before anything important leaves the company.
Evals and observability
Real cases every change is measured against, plus traces of what each agent did, with which data and at what cost.
Security and policies
Least-privilege permissions per agent, secrets management, sandboxed execution and prompt injection defenses.
Model routing and cost control
The best model for each task, with spend caps per flow and a fallback provider when one fails.
Architecture
AI agent harness: the complete overview Turning an LLM into a reliable, safe and capable agent.
1What is a harness?
The runtime system around an LLM that orchestrates execution and manages tools, memory, state, safety and observability.
Agent = Model + Harness
Modelreasoning+Harnesscontrol plane
2Core capabilities
- Prompt & context assembly
- Agent loop orchestration
- Tool orchestration
- Memory management
- Policy & safety enforcement
- Human-in-the-loop
- Sandboxed execution
- Multi-agent coordination
- Observability & tracing
- Evals & improvement loop
Goal: Let the model plan, use tools, remember, execute and deliver results safely.
- User / API
- Harness
- Model
- Tools / environment
- Results
3Harness architecture
User / APIWeb, mobile, CLI
Harness · control plane
Prompt assemblerAgent loop engineTool registryMemory managerPolicy engineSession & stateRetry & recoveryHuman approvalMulti-agent routerTracing & telemetry
LLMReasoning engine
Tools
- Web search
- Databases
- File system
- Email / Slack
- Code execution
- APIs
- Cloud services
- CRM / ERP
Sandbox · execution environment
ContainerDockerMicroVMFirecrackerWASMruntimeNetworkcontrolsFilesystem
Isolation · Resource limits · State snapshots · Security
Memory layers
- Short-term · session
- Long-term · vector DB
- Episodic · traces
- Knowledge base
Key takeaway: The model thinks. The harness makes that thinking executable, safe and reliable.
4The agent loop
- 1. Think
The model analyzes the context and decides what to do next.
- 2. Plan
It chooses: answer, or call a tool.
- 3. Act
The harness executes the tool call safely.
- 4. Observe
Tool output and environment feedback come back.
- 5. Reflect
Results are added to the context; repeat until done.
- 6. Answer
The final output is returned to the user.
5Multi-agent orchestration
Supervisor / router agent
Research agentCoding agentReview agent
Shared memory, knowledge and tools
6Observability & evals
What we trace
- Prompts & completions
- Tool calls & results
- Latency & tokens
- Errors & retries
- Agent transitions
- Memory reads / writes
Trace storelogs, metrics, traces
Improvement loop
- Define tasks and benchmarks
- Run the agent
- Score results
- Analyze failures
- Improve prompts, tools, policies, model
7Safety & control layers
- Input validation & sanitization
- Tool permissions & access control
- Dangerous action detection
- Human approval workflows
- Secrets & data protection
- Rate limits & spend caps
- Audit logs & compliance
8End-to-end example
- The user asks: "Analyze sales.csv and give me insights"
- The harness builds the context: memory, tools, instructions
- The LLM reasons and calls a Python tool to analyze the file
- The sandbox runs the code safely and returns results
- The LLM interprets the results and keeps reasoning
- The harness returns the final answer to the user
How we work
From diagnosis to evolution, with deliverables at every step.
- 01Diagnosis
We map processes, systems, data and risks. You get a prioritized process and clear success criteria.
- 02Design
We define agents, memory, rules, approval roles and evals. You get the architecture and the plan.
- 03Build
We stand up the harness and the first flow on your systems, with weekly reviews on real cases.
- 04Production
We launch with real users, live approvals, traces and alerts, measured against the agreed evals.
- 05Evolution
We tune with operating data and add new agents on the same foundation.
What you get
A system in production, not a prototype.
- Harness deployed on your infrastructure or cloud
- First agentic flow running with real users
- Business memory documented and under your control
- Eval suite built from your own operations
- Dashboard for traces, costs and alerts
- Operations runbook and handover to your team
Technology
The best model for each task, on a shared foundation.
OpenAIClaudeGeminiOpen-source modelsGraph-based orchestrationStructured and vector memoryAutomated evalsTraces and telemetrySandboxed executionSecrets managementAPIs and webhooksCRM and ERP integrationCost control per flow
Further reading
Related articles
Harness engineering
AI Agent Harness Components: What Each Piece Does and Which Vendors Solve It
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AI Harness Development Week by Week: What a Real Project Looks Like, From Diagnosis to Production
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How to Evaluate an AI Harness Partner: The Questions to Ask and the Red Flags to Watch For
How to evaluate an AI harness partner: key questions, red flags and a practical table to choose who will take your AI agents from pilot into production.
Frequently asked questions
What is an AI harness and why do I need one?
It is the infrastructure around the AI model: what information it sees, which tools it uses, which rules it follows, who approves its work and how it is logged. The model reasons; the harness makes that reasoning reliable inside your company.
Can you build on the agents or pilots we already have?
Yes. We start by diagnosing what works and what is missing, and integrations, data and prompts can often be reused. What changes is the foundation: shared memory, approvals, evals and traces for every agent.
Which AI models do you work with?
The best one for each task: OpenAI, Gemini, Claude or open-source models. The harness routes each step to the right model and lets you switch providers without rebuilding the system.
Who owns the memory and the data?
You do. The business memory lives in your infrastructure or your cloud account, under your permissions. We do not sell licenses or lock you into a platform.
Which agent or pilot do you want in production? Tell us about the process and we will propose a diagnosis.
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