Agentic Coding for Tech Leads: Faster Drafts, Same Standards

If you're a tech lead, you already have coding agents on your team — informally (and definitely not on payroll). Someone is using one to draft a refactor, summarize a failing test, or crank out boilerplate. The difference between providing a quiet productivity boost or a new source of churn isn't the model; it's whether you treat agent use like a production workflow or a magic trick.

If you've ever opened a pull request that looks fine and then realized it touched 38 files because the agent ran a formatting sweep you didn't ask for, you already get the failure mode.

Here's the shift that matters: Agents don't produce perfect work, but they do make first drafts cheaper. Your job is to turn that cheap draft into a reliable end result.

The failure pattern you'll see most often is predictable. An agent ships something that looks plausible and might even pass tests, but the change is wrong in intent or risky in its scope of impact. When you dig in, the root cause isn't that the agent is "dumb." It's one of four things:

• The task was underspecified (no clear definition of "done").
• Constraints were missing ("don't touch X" wasn't said).
• Context was mispackaged (too little, too much, or the wrong files).
• Verification was weak (no test, no repro, no behavioral check).

The practical move is to standardize how you hand work to agents and to tighten your gates.

A simple template gets you most of the benefit. It's boring on purpose.

1. Goal: State the behavior change in one sentence — not "refactor module" but "reduce p95 latency of endpoint X by 20% without changing responses."
2. Acceptance criteria: List concrete checks that prove the change is correct. Examples include "add regression test for bug," pnpm test passes, "no changes to schema," "p95 doesn't regress," "works with feature flag off." Gut-check it by imagining you're going over the list with a teammate.
3. Constraints/non-goals: Tell the agent what must not happen — "touch as few files as possible," "no dependency upgrades," "no formatting sweep," "don't rename public APIs."
4. Context pointers: Point to entry points, including the specific files, modules, routes, or test suites. Include gotchas such as flags, migration rules, and rollout constraints. Don't dump the whole repo.

When you require this structure (in the ticket or pull request description), humans execute better too.

Next, adopt an escalation ladder so you don't end up with one "super agent" doing everything.

• Triage agent: Clarifies requirements, gathers context, drafts acceptance criteria, and proposes a plan
• Implementation agent: Implements the plan, runs checks, and iterates to green
• Review agent: Scans for risk (security, perf, edge cases, migrations) and flags scope creep
• Human review: Validates intent and tradeoffs, approves risk, and ensures reality matches the spec

This workflow prevents the most expensive outcome: a confident diff that optimizes for the prompt rather than the product. It also gives you a place to put the inevitable moment where someone asks, "Can we ship this today?" and the honest answer is, "Show me what we verified."

Now, the gates. If you want speed without quality loss, these should be your non-negotiables:

• No merge without automated checks appropriate to the repo (tests, typecheck, lint).
• Every agent-assisted pull request must include acceptance criteria and a short "what I verified" section.
• Prefer minimal diffs; block opportunistic refactors unless explicitly in scope.
• High-risk areas require rollout discipline (feature flags, canaries, staged deploys, SLO monitoring).

Agents increase throughput, but that also increases the rate at which you can create future problems. The gates keep "we shipped" from turning into a Monday-morning regression hunt.

Measure outcomes for a realistic success rate:

• Cycle time (start \-> merge, merge \-> deploy)
• Rework rate (follow-up fixes within seven days)
• Review load (time/pull request, queue size)
• CI health (flake rate, time-to-green)
• Change failure rate (rollbacks, incidents tied to changes)

If cycle time improves but rework or incidents spike, don't blame the model. Tighten specs, improve tests, and reduce diff size.

Finally, roll it out in a way that builds trust:

• Week 1-2: Pick one workflow (small bugfixes or low-risk internal features). Enforce the template, and require a regression test for each bug.
• Week 3-4: Add review-agent passes and codify guardrails. Turn repeated mistakes into checks (lint rules, CI assertions, PR checklist items).

The takeaway for tech leads is simple: Agentic coding is a workflow upgrade, not a typing upgrade. If you standardize inputs (specs), enforce verification (gates), and adopt escalation (roles), you'll get faster drafts and steadier delivery — without paying for speed with churn later.