Managed Agent Harness
We build and run the harness your AI agents need
A model on its own is not an agent. The harness around it (tools, memory, guardrails, human review and audit logs) turns it into one. Tekscape, a Managed Intelligence Provider (MIP), builds that harness and runs it for you as a managed service, on hardware you own or in Microsoft Azure.
An AI agent is a model plus a harness. The harness is the software around the model: tools and connectors, memory, guardrails, human review, evaluation, audit logs and model routing. Tekscape, a Managed Intelligence Provider (MIP), builds and runs that harness as a managed service, on hardware you own, in Microsoft Azure or a mix of both, so your AI agents work on real business data under real controls.
The idea
Agent = model + harness
A language model predicts text. It cannot open a ticket, read a contract or check a policy by itself. What makes it an agent is the harness: the software that gives it tools, memory, rules and oversight.
LangChain puts it plainly in its post "The Anatomy of an Agent Harness": Agent = Model + Harness. The term "agent harness" came into wide use only recently, and the practice of building one is often called harness engineering.
It is tempting to pick a model and stop there. The model is the easy part. The harness decides whether an agent is safe, accurate, affordable and auditable. That is the part we build and run.
What is in the harness
The components we build, run and maintain
Tools & connectors
Scoped, least-privilege access to the systems your AI agents need: Microsoft 365, your PSA or CRM, file shares, databases and line-of-business APIs.
- Read and write permissions set per agent
- Credentials held by the harness, never the model
Memory & context
Agents answer from your business, not the open internet. We connect them to your documents and to a managed semantic model of your terms, rules and metrics.
- Retrieval over approved sources only
- Grounded in your business ontology
Guardrails & policy
Guardrails define what each agent may see, say and do. Policy runs in the harness, outside the model, so the rules do not depend on the model obeying a prompt.
- Data-loss and scope controls
- Blocked actions and topics per role
Human review
By default, anything that sends, pays, deletes or commits goes to a person first. Approvals happen where your team already works.
- Approval gates on high-impact actions
- Clear escalation when an agent is unsure
Evaluation & testing
Every agent has a test set drawn from your real work. We run it before launch and after every model, prompt or data change.
- Regression tests on each release
- Accuracy tracked over time, not assumed
Observability & audit logs
Every prompt, tool call, decision and approval is logged. You can see what an agent did, why, and who signed off.
- 24/7 monitoring of agent health
- Audit trail you own and can export
Cost & model routing
Each task goes to the right-sized model. Routine work runs on the models in your deployment, on your hardware or in Azure. A task goes to a cloud model only when that is cheaper or clearly better.
- Per-task model choice, reviewed regularly
- Spend you can forecast, not a surprise bill
How it fits together
The model sits in the middle. The harness does the rest.
Model
Open-weight on your hardware, or a cloud model reached through routing.
Tools
The systems and APIs the agent may call.
Memory
Context it keeps between tasks.
Guardrails
What it must never do, enforced in code.
Human review
Approval before anything leaves your boundary.
Evals
Tests run before every change ships.
Observability + audit
A log of every action and why.
Cost routing
Your deployment by default, other models when cheaper.
Illustrative. Components are configured per agent and per business.
What we run
The AI agents we build and manage
Workflow automation
Agents that take repeatable back-office work (intake, triage, data entry, follow-ups) off your team's plate, with a person approving anything that leaves your boundary.
Business intelligence
Agents that answer questions about your numbers in plain English, grounded in governed metric definitions so the answer is built from the same definitions as your reports.
Security & compliance
Agents that watch alerts, check configurations against policy and draft evidence for audits, with every step logged.
Knowledge assistants
Agents that find and summarize answers from your own documents, policies and past work, and cite where each answer came from.
Vendor & contract intelligence
Agents that read contracts, invoices and renewals, flag terms and dates that need attention, and compare what you pay with what you signed.
Microsoft 365 Copilot agents
Copilot agents configured, grounded and governed inside your Microsoft tenant: the cloud option in a hybrid setup, held to the same approval and audit standards.
How we start
A six-week path from pilot to production
- Weeks 1-2
Discover and design
We pick one high-value workflow, map the systems and data it touches, and define what the agent may and may not do. We agree on the tests it must pass.
- Weeks 3-4
Build the harness and pilot
We connect tools, set guardrails and approval gates, ground the agent in your data and run it with a small group. Every action is logged and reviewed.
- Weeks 5-6
Harden and go live
We tune against the test set, confirm cost and routing, train your team and move to production. Then we run it: monitoring, updates and evaluation as an ongoing managed service.
Where it runs
On your hardware, hybrid or in Azure
| Option | Where models run | Best for |
|---|---|---|
| On-site | Local models on hardware you own, typically an NVIDIA DGX Spark, in your office or rack | Sensitive data, steady workloads, predictable monthly cost |
| Hybrid | Local models by default; cloud models for tasks where they are cheaper or clearly better | Most businesses: control where it matters, flexibility where it pays |
| Microsoft Azure | Models in your Azure subscription on Microsoft Foundry, plus Microsoft 365 Copilot, under our governance | Low or bursty volume, or teams already standardized on Microsoft 365 and Azure |
Local is not always cheaper. We will tell you when it is not.
Running models on your own hardware buys control and predictable cost. It does not promise savings. For low volume or occasional tasks, a cloud model is often the better deal, and our routing sends those tasks there.
The harness is what makes that choice safe: the same guardrails, human review and audit logs apply whichever model does the work.
Frequently asked questions
Sources
- LangChain: The Anatomy of an Agent HarnessSource of the Agent = Model + Harness framing.
- Wikipedia: Agent harnessBackground on the term; who coined it is disputed.
Start with one agent, run properly
Bring one workflow. We will map it, show you the harness it needs and tell you honestly whether it should run on your hardware, in the cloud or both.
Book a free AI outcomes sessionKey facts
General information, not legal advice. Confirm your obligations with counsel.
NIST's AI Risk Management Framework (AI RMF 1.0) is voluntary and non-sector-specific, giving organizations of all sizes and in all sectors flexibility to apply it.
Source: NIST AI 100-1, Artificial Intelligence Risk Management Framework (AI RMF 1.0), 2023-01-26
The AI RMF organizes AI risk work into four functions: Govern, Map, Measure and Manage.
Source: NIST AI 100-1, Artificial Intelligence Risk Management Framework (AI RMF 1.0), 2023-01-26
NIST AI 600-1 (July 2024) is a cross-sectoral Generative AI profile that serves as a companion to the AI RMF.
Source: NIST AI 600-1, Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile, 2024-07-26