The “Explicit Era” We All Know
For the last decade, IT managers have juggled two resource pools that share a hidden dependency: they both demand extremely detailed instructions.
| Resource Pool | What You Must Provide | Why It’s a Management Headache |
|---|---|---|
| Contract Developers (on-shore or off-shore) | – Exhaustive requirements documents – Up-front interface specs – Weekly clarification calls | Onboarding time + change-request churn = higher lead time |
| 1st-Gen AI Coding Tools (autocomplete, snippet generators) | – Prompt that describes the entire function or file – Inline code comments for every edge case | Prompts become mini-specs; any ambiguity produces hallucinations you still have to review |
Both groups live or die on explicit instruction. If a requirement is missing, the default outcome is rework—whether that’s a ticket back to the vendor or a GPT suggestion that compiles but violates your API gateway rules.
What’s Changed: Agents That Read the Repo
New frameworks such as GitHub Copilot Workspace capture a natural-language goal (“add dark-mode support”), scan the repository, propose a plan, and iteratively modify code until tests pass—no line-by-line prompt required. [1]
Research prototypes like Microsoft’s AutoDev show similar behavior, orchestrating builds, git operations, and multi-file edits from a single objective statement. [2]
Why is this possible now?
- Larger context windows let advanced models load entire projects plus ADRs in one shot.
- Higher reasoning scores—SWE-bench success rates jumped from 4 % in 2023 to ~69 % in 2025. [3]
- IDE integrations stream live file paths, test output, and diff hunks to the model, so it learns conventions instead of asking.
The Symbiotic Model: People Set Vision, Agents Fill the Gaps
| Dimension | Contract Devs & 1st-Gen AI (Explicit) | Next-Gen Dev Teams + AI Co-Agents (Implicit-Ready) |
|---|---|---|
| Input | Detailed SOW / prompt | High-level intent, ADRs, principles |
| Context Discovery | Human Q&A or none | Agent scans repo, tests, CI logs |
| Feedback Loop | Weekly reviews / single-shot snippet | Continuous draft–test–fix cycles inside IDE & CI |
| Who Guards Standards? | Manual PR review & linters | Agents flag policy violations while coding |
| Manager’s Role | Write specs, track hours | Curate architecture docs, measure flow & quality |
Management Wins You Can Bank On
- Shorter Lead Time
A developer can drop a one-sentence objective into Copilot Workspace and get a tested PR in minutes—dramatically shrinking the design-to-deploy window. - Fewer Clarification Loops
Agents derive naming conventions and error-handling rules from existing files; contractors used to Slack you at 2 a.m. for the same info. - Governance at Scale
Because agents “understand” your repo-wide patterns, they enforce them automatically—catching a rogue SQL call before it hits review. - Cost Reallocation
Money once spent writing exhaustive specs shifts to higher-value tasks: curating Architecture Decision Records, evolving platform standards, and up-skilling staff to orchestrate agents.
Your Transition Playbook
| Step | Action | Managerial KPI |
|---|---|---|
| 1. Codify Intent | Convert tribal knowledge into ADRs, style guides, and test suites. | % of repos with current ADR index |
| 2. Pilot an Agentic IDE | Enable Copilot Workspace or similar on one product line. | PR cycle time vs. baseline |
| 3. Redesign Reviews | Focus on architectural fit & security, not brace placement. | Defect density in prod |
| 4. Upskill Devs as “Prompt Architects” | Train teams to phrase objectives, not just functions. | # objectives delivered per sprint |
| 5. Establish Guardrails | Automate policy checks in CI, so agents get instant feedback. | Policy-violation rate over time |
Final Thought
The shift from explicit to implicit isn’t about removing humans; it’s about elevating them. Contract coders and early AI relieved us of some typing, but they still needed babysitting. The new generation of AI co-agents thrives on the strategic signals you already manage—vision, principles, and measurable outcomes. Turn those signals into living documentation, and your teams will deliver faster, safer, and with far less midnight Slack.
Note: Content created with assistance from AI. Learn More
