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AI coding agents are unlikely to remove people from software delivery. They can produce code faster, but the responsibility for context, testing, security, release decisions and business risk remains with human teams. A London panel at Harness Unscripted on October 9, 2026, argued that enterprises should increase agent autonomy only when deterministic controls, reviewable evidence and downstream delivery capacity are ready.
The practical question is not whether an agent can open a pull request. It is whether your organization can show what the agent knew, which rules it followed, what checks ran, who accepted the residual risk and whether integration and release teams can absorb the extra throughput.
What the Harness Unscripted panel says about humans and agents
The panel brought together Sean Hamilton-Andrews, director of platform engineering for hosting and service platforms at Aviva; Sigurd Pollmann, senior vice president and head of DevSecOps toolchain and operations at Deutsche Börse; Lina Moufarreh, a member of technical staff at Anthropic; and Chris Ibbitson, a principal solutions architect at AWS. Their common position was not anti-automation. It was that software delivery is a socio-technical system, and increasing the speed of one activity does not eliminate the controls and decisions around it.
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| Term | Meaning in agentic delivery | Why it matters |
|---|---|---|
| Context | The business and technical information placed in the model’s working reach at inference time. | Without relevant context, an agent can make a locally plausible change that violates architecture, ownership or compliance requirements. |
| Harness | The controls around the agent, including tool choices, credentials, policies, tests and authorization boundaries. | It limits unauthorized actions and makes the agent’s work observable and repeatable. |
| Evidence | Recorded proof of the context used, policy versions, tool calls, test results, scans, approvals and rollout decisions. | Reviewers can judge the actual change rather than trusting an opaque assertion that it is safe. |
Jon Reed’s October 11, 2026 follow-up described context as getting the right data into an LLM at inference time and a harness as the controls that hold an agent accountable. That distinction is useful: better prompts alone do not create governance, and governance without usable context produces cautious but ineffective automation.
Why faster code generation does not guarantee better software
Code creation is only one segment of a delivery path. An agent may generate ten changes while a shared integration environment can validate only two, or while a security team can triage only a handful of findings. If those queues are ignored, the organization has accelerated work in progress, not customer value.
Quality can also fall when human attention is treated as the expendable part of the process. Generated code may compile and pass narrow unit tests while introducing an authorization gap, a data-handling violation, an incompatibility with an older client or an operational burden that was not represented in the prompt. Human reviewers provide system context, challenge assumptions and decide whether the remaining uncertainty is acceptable.
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- Planning: confirm the problem, affected services, data sensitivity and acceptable blast radius.
- Implementation: constrain repository access, dependencies, tools and credentials.
- Verification: run deterministic unit, integration, security and policy checks.
- Review: present a traceable evidence package to the appropriate human role.
- Release: use a rollout strategy that limits exposure and provides rollback.
- Operations: watch production signals and feed incidents or near misses back into policies and context.
The three jobs platform teams must design for
1. Reduce risk
Risk reduction starts with least privilege. An agent that edits application code rarely needs unrestricted production credentials, the ability to disable security checks or access every repository. Separate read, write, test and deploy permissions; issue short-lived credentials; and make high-impact tools require an explicit approval step.
Deterministic controls should fail closed. A policy that blocks secrets in a commit, prohibits unreviewed infrastructure changes or requires a signed artifact must be enforced by the pipeline, not merely described in a document an agent may or may not read.
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2. Establish accountability
Every agent action needs an accountable owner. The person who invokes an agent is not automatically the only responsible party: service owners, security approvers, release managers and the manager who authorized the use case may each have defined duties. Record the initiating user, agent identity and version, repositories and tools accessed, policy versions, test and scan results, approvals, deployment stages and final outcome.
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3. Manage bottlenecks
Platform teams should model the complete value stream before increasing agent throughput. Measure queue time and capacity in integration environments, security scanning, test execution, release operations and change-advisory processes. If one stage is saturated, adding more autonomous coding increases waiting time and context switching at that stage.
Give an agent usable context, not a document dump
Hamilton-Andrews said teams should make information available in a form an agent can read and use with engineers, then bring those skills and context into the agent harnesses. The useful unit is not “all documentation”; it is the small set of current facts needed for a decision.
Context worth curating
- Service ownership, on-call contacts, dependency boundaries and interface contracts.
- Architecture decisions, supported runtime versions and approved libraries.
- Data classification, retention rules, privacy constraints and geographic restrictions.
- Threat models, security patterns, prohibited operations and incident runbooks.
- Testing conventions, required environments, release windows and rollback procedures.
- Business objectives and acceptance criteria expressed in testable terms.
Keep this material versioned and scoped. A payment service agent should receive payment-service policies and current dependency facts, not an unfiltered knowledge base containing contradictory guidance. Include freshness metadata and an owner so a reviewer can tell whether the context was current when the agent acted.
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Make the pipeline deterministic and reviewable
Hamilton-Andrews’s requirement was that a reviewer should see both the context the agent had and the checks it already passed. Build a review packet automatically rather than asking an engineer to reconstruct events from chat logs.
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- Pin the agent configuration: record model or agent version, system instructions, tool definitions and repository revision.
- Declare permissions: show credentials, environments and operations available to the run, including denied operations.
- Capture the plan and diff: retain the requested outcome, files changed, dependencies added and generated commands.
- Run reproducible checks: execute unit and integration tests, static analysis, dependency and secret scans, infrastructure policies and license checks as applicable.
- Attach results immutably: store logs, artifact hashes, failures, retries and policy versions with the change record.
- Gate transitions: define which results permit automatic merge, which require evidence review and which require named approval.
Determinism does not mean every agent response is identical. It means the organization can reliably enforce the same acceptance criteria and explain why a change advanced or stopped.
Scale autonomy by risk tier
Moufarreh proposed separate lanes for different levels of risk. The right lane depends on blast radius, reversibility, confidence in tests and the organization’s regulatory obligations, not on whether the code was written by a person or an agent.
| Risk lane | Typical work | Agent authority | Human involvement | Release method |
|---|---|---|---|---|
| Low | Documentation, isolated refactoring, dependency updates with strong tests and no sensitive-data or infrastructure impact. | May create and merge a change when all required checks pass. | Informed through an auditable record; spot checks remain possible. | Automatic merge and normal deployment pipeline. |
| Medium | Changes crossing services, modifying behavior or touching non-critical configuration. | May implement and test, but cannot merge without presenting evidence. | Reviewer examines context, diff, test and scan results and accepts residual risk. | Evidence review followed by a controlled deployment. |
| High | Authentication, payments, regulated data, production infrastructure, safety-relevant logic or changes with wide blast radius. | May propose work and run approved tools; deployment and policy overrides are prohibited. | Named approvers perform comprehensive review, with security or compliance involvement where required. | Staged rollout, enhanced monitoring, explicit rollback and post-release confirmation. |
Risk classification should be recalculated when a change expands in scope. A harmless-looking library update can become high risk if it alters cryptography, handles personal data or affects a shared runtime. Conversely, a high-risk service may contain a low-risk documentation-only change, provided the controls verify that claim.
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Evidence is the mechanism for trust
Evidence turns human oversight from a vague request to an operational practice. For each agent-assisted change, a reviewer should be able to answer five questions: What was the intended outcome? What information and policies were available? What did the agent actually do? Which independent checks ran and with what results? Who accepted the remaining risk?
In a regulated organization, retain this record for the period required by the applicable control framework and protect it from silent alteration. Link evidence to the source revision and deployment artifact so an auditor or incident investigator can reproduce the decision path. If a check was skipped, record the reason and the approving authority rather than presenting an incomplete packet as a clean result.
Where AI-generated code creates new bottlenecks
Ibbitson urged teams to ask not only how code is written, but how integration testing, security scans, unit tests and release to customers will work at higher volume. Hamilton-Andrews similarly warned that solving one queue can simply push the bottleneck to the next stage.
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| Stage | Warning sign after agent adoption | Capacity response |
|---|---|---|
| Unit tests | Longer execution queues, flaky suites or developers bypassing slow checks. | Parallelize safely, remove flakiness, cache deterministic work and set a maximum acceptable queue time. |
| Integration tests | Shared environments are constantly occupied and changes wait for scarce test data. | Create isolated ephemeral environments, prioritize by risk and expand representative test fixtures. |
| Security scans | Finding backlogs grow faster than security teams can triage them. | Automate high-confidence remediation, tune duplicate findings and reserve human analysis for exploitable or high-impact issues. |
| Release operations | Artifacts are ready but deployment windows, change records or operators are unavailable. | Standardize release evidence, automate low-risk promotion and schedule capacity for exceptional changes. |
| Change-advisory boards | Meeting agendas fill with low-value approvals while high-risk work receives less attention. | Use pre-approved, evidence-complete lanes for routine changes and focus board time on novel blast radius and residual risk. |
The panel used a “10x performance improvement” as a planning scenario for finding stages that could not absorb AI-driven throughput. It was not a measured adoption result, productivity statistic or return-on-investment claim. Use the scenario to run capacity exercises, not to promise that every team will become ten times faster.
A practical rollout plan for enterprises
1. Inventory agent use cases
List proposed tasks by repository, environment, data type, reversibility and customer impact. Start with bounded work where a failed change is easy to detect and roll back.
2. Assign risk and ownership
Define the risk lane, service owner, security contact, release authority and escalation path before enabling the agent. Document who can approve exceptions.
3. Prepare authoritative context
Curate current architecture, policy, testing and operational material. Remove contradictory instructions, assign owners and expose only the context needed for the task.
4. Build the harness
Configure repository boundaries, tool allow-lists, credential scope, network access, spending limits and approval gates. Test denied actions as deliberately as permitted ones.
5. Automate the evidence packet
Capture prompts or task requests, context references, tool calls, diffs, artifacts, test and scan results, policy decisions and approvals in one change record.
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6. Pilot the lowest-risk lane
Measure escaped defects, rollback frequency, review time, queue time and policy violations. Do not expand autonomy merely because code output increased.
7. Exercise downstream capacity
Run load scenarios against integration environments, scanners, release staff and change governance. Remove bottlenecks before opening additional agent lanes.
8. Increase autonomy gradually
Promote a task category only when evidence quality, deterministic checks and operational capacity meet written thresholds. Keep a rapid rollback path and pause authority.
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9. Review incidents and near misses
Feed failures back into context, policies, tests and risk classification. An agent that repeatedly encounters the same ambiguity needs better constraints, not simply more human vigilance.
Human responsibility does not disappear when a machine acts
Pollmann described moral judgment and work ethics as the last line of defense, noting that a machine does not possess them. In practical terms, an organization remains accountable for an agent’s result even when no person typed the final code. The individual who directed the work, the owner who accepted the change and the leader who authorized the operating model must understand their respective responsibilities.
That principle rules out rubber-stamping. A reviewer should have enough time, expertise and evidence to challenge the change; otherwise the approval is ceremonial. It also rules out blaming the tool after an incident. The remedy is to improve authorization boundaries, context, tests, evidence and escalation—not to pretend that the agent made an independent business decision.
Common failure modes and fixes
- Context dumping: Supplying huge, stale document sets creates conflicting instructions. Curate scoped, versioned context with named owners.
- Prompt-only governance: Asking an agent to follow rules without pipeline enforcement leaves a bypass path. Encode critical policies as deterministic gates.
- Opaque approvals: A green status without logs, policy versions or artifact identity is weak evidence. Generate a complete review packet.
- Uniform autonomy: Treating a typo fix like an authentication change ignores blast radius. Assign lanes and recalibrate when scope changes.
- Reviewer overload: Sending every generated change to the same specialists creates a queue. Automate low-risk work and reserve experts for uncertainty and impact.
- Downstream neglect: Expanding code generation while integration, scanning or release capacity stays fixed moves the queue. Capacity-plan the entire value stream.
The central lesson from Harness Unscripted is straightforward: agents can increase delivery velocity, but people still set intent, supply context, enforce controls, examine evidence and accept risk. Safe adoption is therefore an operating-model change, not a feature toggle.
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