Context and memory
Define session state, long-term memory, retrieval sources, project separation, retention, deletion, and who can inspect stored context.
The control layer around an AI model
Choose an AI agent harness for the work, controls, and operations you need.
AI Impact Maine helps Portland and statewide organizations evaluate the runtime that connects models to business records, memory, tools, approvals, logs, and multi-step workflows.
Model routing
Tool boundaries
Memory design
Reviewable activity
Harness Responsibilities
The model is only one component of an agent.
The harness determines how instructions are assembled, which records are retrieved, how state persists, which tools are exposed, when a person must approve work, and what evidence remains afterward.
Tools and credentials
Expose only approved functions and narrowly scoped identities. Separate read, draft, send, update, execute, and delete capabilities.
Models and routing
Route work to a local model, private-cloud endpoint, or approved enterprise API based on capability, sensitivity, cost, and latency.
Approvals and policies
Pause consequential actions for a named reviewer and provide enough context for a meaningful approve, reject, or revise decision.
Logs and evaluation
Capture supported activity without unnecessarily retaining full prompts, secrets, private transcripts, or unrelated business records.
Lifecycle and recovery
Plan updates, dependency review, rollback, queued work, failed tasks, emergency shutdown, and version documentation.
Agent Harness Selection and Pilot
- Workflow and control requirements
- Harness and deployment-option comparison
- Model, memory, retrieval, and storage architecture
- Approved tools and credential map
- Human approval and escalation design
- Agent and sub-agent boundaries
- Logging, testing, rollback, and shutdown plan
- One bounded pilot and written operating handoff
Engagement Process
Select against requirements, not popularity.
Define the job
Describe data, actions, users, integrations, autonomy, approvals, deployment location, and operating constraints.
Compare control surfaces
Review tool isolation, identity, memory, model support, logs, testing, updates, licensing, and shutdown behavior.
Configure one pilot
Use least privilege, minimized data, approved connectors, controlled tests, and a named human owner.
Document the decision
Record dependencies, limitations, operations, change control, support boundaries, and exit options.
Common Questions
AI agent harness FAQ
What is an AI agent harness?
An agent harness is the runtime and control layer that connects an AI model to instructions, memory, tools, files, applications, approvals, logs, and other agents. It determines much of what the agent can observe and do.
Is the harness the same as the AI model?
No. The model produces responses or decisions; the harness assembles context, calls the model, manages state, exposes tools, handles approvals, and records activity. Either component may be local or cloud-hosted.
Can an organization change models later?
Sometimes. Model portability depends on the harness, tool interfaces, prompt design, memory format, provider features, and test coverage. A deployment should document dependencies before model changes.
Put a deliberate control layer around agent work.
Compare harnesses using one bounded workflow and the controls your team must operate.