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Harness Engineering
Trustworthy AI in production, not just in demos
73% of AI projects never reach production. The reason is never the model — it's the "harness": the infrastructure around the agent. We design, instrument and operate the scaffolding that makes your AI agents trustworthy.
The Concept
Agent = Model + Harness
When an AI agent fails in production, it's almost never the model's fault. It's the harness: the set of tools, context management, memory, evaluations, guardrails and observability around the model.
Anthropic and OpenAI popularized the term to describe a new discipline: stop optimizing isolated prompts and start designing the complete system that makes an agent work consistently, observably and safely over time.
At SISCON we apply this discipline to the agents we build — and also to agents that other teams have already deployed and need to "industrialize".
Services
What we build in your harness
Four work areas that can run together or separately depending on where you are in your AI journey.
🏗️ Scaffolding and Agent Design
We define the architecture before the first prompt: versioned system prompts, well-typed tool schemas, sub-agents with bounded responsibilities and an AGENTS.md documenting architectural rules the agent respects by default.
🧠 Context Engineering and Memory
Smart context compaction for long sessions, RAG over your corporate sources with ChromaDB, progress files to coordinate multiple contexts, and context-isolation patterns.
📈 Evaluation and Observability
Automated evals pipelines (golden sets, LLM-as-judge, regression tests), full traceability with Langfuse, quality dashboards per use case and alerts when a new model version degrades performance.
🛡️ Safety, Guardrails and Cost Control
Defense-in-depth with independent layers (input validation, output filters, sandboxes for dangerous tool use, human-in-the-loop on critical steps), per-task budgets and circuit breakers for anomalous behavior.
🔗 Related services: Harness Engineering is the operational layer that makes AI Agents and Intelligent Automation productive. If you already have agents in pilot but can't get them to production with SLA, this is the service you need. If you don't have agents yet, start with AI Strategy Consulting.
Methodology
Guides + Sensors: the feedforward/feedback model
We adopt the harness engineering framework that Thoughtworks, Anthropic and OpenAI have published: every agent behavior is controlled by a guide (before acting) and a sensor (after acting).
🎯 Guides (feedforward)
Anticipate the agent's behavior and guide it before it acts. They increase the probability of getting it right on the first try.
Examples: Structured system prompts, AGENTS.md with domain rules, explicit tool descriptions, invocation examples (few-shot), mandatory planning templates.
🔎 Sensors (feedback)
Observe after the agent acts and let it self-correct.
Examples: Custom linters, output schema validators, post-generation unit tests, LLM-as-judge evals, reviewers that escalate to humans when confidence is low.
Process
How We Work
We Audit
Mapping of the current harness: which guides and sensors exist, what's missing.
We Design
Proposal for scaffolding, evals, observability and guardrails.
We Instrument
Iterative implementation with Langfuse, evals pipelines and cost controls.
We Operate
Continuous monitoring, tuning of guides/sensors and response to regressions.
Use Cases
Where the harness makes the difference
Typical scenarios where our clients move from "we have a demo" to "we have a product".
🔧 Code agent in production
Harness with specialized sub-agents (plan/code/review/test), sandboxes for safe execution and automatic regression on every change. Typical result: -60% errors in PRs, -40% cost per task.
🎫 Support with guaranteed SLA
Context engineering over internal KB, nightly evals against golden set, deterministic fallback when confidence is low and published deflection metrics.
🔬 Long-running research agents
Cross-session progress files, compaction with key-decision preservation, full source traceability and citation verification.
📑 Critical document processing
Schema validation on outputs, human-in-the-loop on configurable thresholds, full per-document audit and reproducibility for compliance.
⚙️ Multi-agent orchestration
Explicit contracts between agents, versioned shared memory, cross-agent observability and circuit breakers to prevent error cascades.
💸 Cost reduction in existing agents
Audit of your current harness, routing to cheaper models for simple steps, smart call caching and per-use-case budget ceilings. Savings of 30-60% without quality loss.
Impact
Before and after the harness
| Metric | Without Harness | With Harness | Typical improvement |
|---|---|---|---|
| Success rate on complex tasks | 40-55% | 85-95% | +40pp |
| Cost per task | Variable / leak | Bounded | -30 to -60% |
| Time to detect regression | Days / weeks | Minutes | -99% |
| Security incidents | Reactive | Preventive | Defense-in-depth |
| Per-decision traceability | Limited | Complete | 100% |
FAQ
Frequently Asked Questions
What's the difference between a framework (LangChain, LlamaIndex) and a harness?
A framework gives you the primitives (tool calling, memory, orchestration). The harness is the complete production system around the agent: evals, observability, cost control, guardrails, error recovery.
Do we need Harness Engineering if we only have a simple chatbot?
Probably not. Basic Q&A without tool use doesn't require it. But the moment your agent calls external APIs, executes multi-step workflows or operates without human review of each output, you need at least verification, observability and cost controls.
Can it be applied to agents we already have deployed?
Yes, and it's one of our most common cases. We start with a 2-week audit of the current harness, identify the most critical gaps and close them incrementally without disrupting operations.
What tools do you use?
Langfuse for observability and evals, Pydantic/Zod for schema validation, Docker for sandboxes, Ollama/LiteLLM for multi-model routing, ChromaDB for RAG, and frameworks like LangGraph or the Claude Agent SDK when they fit.
How long does it take?
Audit: 2 weeks. Minimum viable harness (evals + observability + basic guardrails): 4-6 weeks. Mature harness with cross-session memory, defense-in-depth and regression automation: 10-14 weeks.
How does it integrate with the other SISCON AI services?
Harness Engineering is the cross-cutting layer: it makes AI Agents productive, instruments Intelligent Automation and measures the quality of Predictive Analytics models.