Engineering the System Around AI
Turn model capabilities into controlled, observable, production-ready execution.
An AI model can reason about a task, but reliable execution requires more than a prompt. It needs context, tools, state management, runtime controls, verification, permissions, and recovery mechanisms.
We engineer the operational layer that connects AI models to real business environments.
What is agent harness engineering?
The software infrastructure surrounding an AI model
An agent harness is the software infrastructure surrounding an AI model that coordinates its execution and interaction with the outside world.
It manages the loop through which a model receives context, proposes actions, invokes tools, processes results, and determines whether to continue, recover, or finish.
A useful conceptual model
AI AgentModelHarnessToolsEnvironment
- Model
- Contributes reasoning and generation capabilities.
- Harness
- Manages execution.
- Tools
- Expose permitted actions.
- Environment
- Supplies the data, interfaces, policies, and operating conditions in which the agent works.
The precise architecture varies by use case, but the underlying engineering problem remains consistent: how do we make AI behavior sufficiently reliable, secure, observable, and economical for a particular business task?
The components we engineer
Eight layers between a model and dependable work
Execution Runtime
Control how work progresses.
Implement execution loops, structured task states, timeouts, retries, cancellation, concurrency limits, and explicit completion conditions.
For workflows requiring predictable behavior, execution should follow defined transitions rather than allowing unrestricted, open-ended action.
Context Engineering
Give the model the information it needs, when it needs it.
Manage system instructions, conversation history, retrieved knowledge, task state, tool outputs, and context-window limits.
Use summarization, retrieval, structured task artifacts, and progressive disclosure to reduce irrelevant information and preserve critical requirements throughout longer tasks.
Tool Orchestration
Connect reasoning to authorized capabilities.
Expose APIs, database operations, internal applications, code execution environments, and external services through controlled tool interfaces.
Tools should have explicit input schemas, permission boundaries, documented behavior, and appropriate error handling.
Where suitable, Model Context Protocol (MCP) can standardize connections between AI applications and tools.
State & Memory Management
Maintain continuity across execution steps.
Separate temporary working context from durable task state and longer-term memory.
Persist relevant progress, intermediate artifacts, decisions, and checkpoints so that a task can resume after interruption without relying entirely on conversation history.
Memory retention should be governed by the application's privacy, security, and data-retention requirements.
Plan–Execute–Verify
Validate work before treating it as complete.
For appropriate tasks, structure execution around three stages:
- Plan: Establish the intended steps, constraints, and expected outcomes.
- Execute: Perform authorized operations and capture their results.
- Verify: Check the results against explicit requirements before proceeding or reporting completion.
Verification can include schema validation, deterministic business rules, independent model review, source checks, tests, or human approval.
Identity, Permissions & Guardrails
Bound what an agent can do.
Integrate identity and access management, least-privilege permissions, tool-specific authorization, execution isolation, approval gates, and restrictions on sensitive actions.
An agent should receive only the capabilities required for its current task. High-impact actions may require explicit human authorization.
Observability & Evaluation
Make agent behavior inspectable and improvable.
Capture execution traces, tool calls, failures, latency, token consumption, costs, and evaluation results.
Use representative test cases and production feedback to identify recurring errors, regressions, inefficient execution paths, and changes in performance.
Recovery & Long-Running Execution
Design for interruption and failure.
Support checkpointing, idempotent operations where appropriate, bounded retries, error classification, and controlled resumption.
Long-running tasks need explicit progress tracking and durable state, not just an increasingly large conversation history.
Harness engineering and the AI control plane
From one execution loop to an agent ecosystem
As organizations deploy more agents across different teams and applications, the challenge extends beyond individual execution loops.
An AI control plane provides organization-wide coordination and governance for the agent ecosystem.
Agent HarnessManages execution of an agent or workflow
AI Control PlaneCoordinates agents and policies across an organization
Agent HarnessControls context, tools, state, and task progression
AI Control PlaneManages registries, shared governance, and organizational visibility
Agent HarnessHandles retries, verification, and task-level failures
AI Control PlaneSupports fleet-wide monitoring, policy enforcement, and cost allocation
Agent HarnessEnforces execution-level permissions
AI Control PlaneCoordinates identity and access policies across integrated systems
Agent HarnessMeasures task-level performance
AI Control PlaneAggregates operational and business metrics across deployments
These layers can overlap, and not every organization needs a separate control-plane product. The architecture should match the scale, risk, and operational complexity of the deployment.
The economics of reliable AI
Model selection is only one determinant of AI operating costs.
Harness-level engineering can improve economics through:
Context efficiency: Reducing unnecessary input tokens and oversized tool responses.
Model routing: Assigning tasks to models appropriate to their complexity and risk.
Caching: Reusing eligible results or stable prompt prefixes when correctness and provider semantics permit.
Bounded execution: Preventing repeated calls, uncontrolled loops, and unnecessary agent delegation.
Verification strategy: Allocating validation effort according to task risk and failure cost.
Performance monitoring: Tracking cost per successful task rather than token usage alone.
We evaluate these techniques against the target workload. Cost savings and latency improvements depend on the model, provider, cache behavior, task design, and production traffic; they are not assumed in advance.
Environment engineering
Make Your Systems Understandable to AI
Agents are limited not only by model capability but also by the quality of the environment they must navigate.
Unclear APIs, undocumented business rules, inconsistent data schemas, ambiguous permissions, and unpredictable side effects increase execution complexity and failure risk.
Environment engineering addresses these problems by making enterprise systems more explicit and accessible through:
Well-defined APIs and tool contracts.
Clear database schemas and data dictionaries.
Machine-readable documentation and workflow definitions.
Consistent error handling and status reporting.
Explicit business rules and validation requirements.
Test environments and representative evaluation datasets.
Secure, auditable interfaces to legacy systems.
The objective is not to grant AI unrestricted access to existing infrastructure. It is to create controlled interfaces through which AI can perform useful work.
Build vs. buy
Reuse the commodity. Engineer the differentiator.
We distinguish between reusable infrastructure and capabilities that differentiate your business.
Use established infrastructure where practical
Model-serving platforms, agent frameworks, tracing systems, identity services, and standardized integration protocols.
Engineer the business-specific layer
Domain tools, workflow logic, evaluation datasets, specialized knowledge models, environment maps, integrations, and decision rules.
This approach avoids rebuilding commodity components without sacrificing control over the capabilities that make the solution valuable.
When do you need harness engineering?
When an AI system must:
Execute more than a single prompt-and-response interaction.
Work with private enterprise data or authenticated systems.
Maintain state across multiple steps or sessions.
Recover from failures and interruptions.
Produce outputs that must meet explicit quality requirements.
Operate within strict security, compliance, or approval boundaries.
Meet measurable latency, reliability, and cost targets.
For a simple text-generation feature, a full harness architecture may be unnecessary. We engineer the level of infrastructure the application actually requires.
Our approach
Six steps from objective to operation
Define
Define the agent's objective and operating boundaries.
Map
Map its tools, data dependencies, and environment.
Design
Design execution, context, state, and verification mechanisms.
Implement
Implement permissions, monitoring, and recovery.
Evaluate
Evaluate realistic tasks, failure modes, latency, and cost.
Operate
Deploy, monitor, and improve against agreed performance criteria.
Continue exploring
Related capabilities
- Enterprise AI InfrastructureAI Tools & IntegrationsConnect AI to CRM, ERP, databases, documents, and operational systems through controlled APIs, Model Context Protocol (MCP), and secure execution boundaries.
- Enterprise AI InfrastructureAI Control Plane & GovernanceGovern AI across your organization: agent and model registries, identity and access, policy enforcement, observability, release management, and cost tracking.
Build AI That Can Be Trusted to Do the Work
The difference between an impressive demonstration and a dependable AI application is often the engineering surrounding the model.