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Gerry Wolfe
Gerry Wolfe

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The Agentic Manifesto: Why Agile is Breaking in the Age of AI Agents

Software development has slammed into a "speed of light" wall. We're shackling 2001 processes (dare I say the word, Agile) to 2026 technology (autonomous AI agents).

In 2026, agentic AI is now reality. Anthropic's 2026 Agentic Coding Trends Report shows single agents evolving into coordinated teams, long-running agents building entire systems, and cycle times collapsing from weeks to hours. Gartner predicts 40% of enterprise applications will embed task-specific agents by year's end, up from under 5% last year. McKinsey notes software engineering leading scaled adoption at ~24%. When agents generate features, write tests, review code, debug, and deploy in minutes—often in parallel swarms—the two-week sprint isn't a rhythm; it's a relic. Story points aren't metrics; they're outdated guesses (don't we know it). Detailed Jira tickets aren't guides; they're administrative drag (yes sir, they are).

We are shifting from Waterfall (Big Upfront Design) → Agile (Iterative Human Labor) → Agentic (Architected Intent with Autonomous Execution).

The Agentic Manifesto: 4 Core Values

In this era of agentic autonomy, we have come to value:

  1. Human Intent over exhaustive Technical Requirements.

    Humans define vision, goals, and guardrails; agents handle the how.

  2. Continuous Flow over rigid Time-Boxed Sprints.

    Work streams in real-time, with agents shipping validated increments the moment they're architecturally sound—not waiting for arbitrary cycles.

  3. Architectural Integrity over sheer Feature Output.

    Speed without structure breeds chaos; agents must preserve modularity, security, and maintainability through enforced constraints.

  4. Automated Validation over Manual Estimation.

    Agents self-test, self-review, and self-correct via loops; success measures intent accuracy, not velocity or points burned.

1. The Death of the Sprint (and the Birth of Live Continuous Flow)

Sprints suited slow, distractible humans (yes, we're easily distracted by Slack pings, coffee runs even if just to the kitchen, and of course social media) needing predictable windows. Agents don't tire, forget, or demotivate. They execute tirelessly in perception-reasoning-action loops (though high token usage and costs can impose practical limits for us humans that are transferred down to our agent counterparts).

In the Agentic SDLC (or emerging ADLC), work flows continuously. Features deploy as soon as agent swarms validate against architecture, tests pass, and drift is minimal. Waiting for a "Wednesday deployment window" while competitors ship 40x faster? That's self-imposed debt. Anthropic highlights long-running agents building complete systems; multi-agent orchestration is the breakthrough for complex workflows.

2. From "Jira Hell" to "Context Capsules"

Detailed "As a user, I want..." tickets waste human time when agents excel at implementation from high-level prompts. AI needs context, not bureaucracy.

The ticket evolves into a Context Capsule: concise human intent + constraints (e.g., architectural boundaries, security rules, acceptance tests). Agents generate details, iterate via feedback, and log trajectories for traceability. Spending 30 minutes on a ticket an agent completes in 10 seconds? The process is broken.

Emerging tools emphasize intent-based workflows over granular specs. Extensions like ThinkGit — "Git for your thinking" (an extension that I recently published) — take this further by providing version control for AI conversations in Cursor or VS Code. It captures, indexes, and visualizes entire coding sessions, making past prompts, decisions, and evolutions searchable and reusable. This turns ephemeral AI interactions into persistent, increasing knowledge that compounds across projects.

3. The New Stand-up: The "System Pulse"

15-minute sync or even async stand-ups are pre-agentic artifacts. Agents already track commits, logs, trajectories, and drift in real-time.

What if, instead, we had a System Pulse dashboard that surfaces architectural alignment, intent drift, technical debt accumulation, and agent performance? Teams convene not for status, but for high-leverage discussions: Is agent speed introducing subtle brittleness? Does the product's emergent behavior still match business vision? Human oversight scales through intelligent collaboration, per 2026 trends.

4. The Human as "Architect of Intent"

If agents code, review (via peer agents), test, and deploy—what remains for humans?

You are the Architect of Intent:

  • Developers: Orchestrate agent fleets, enforce modularity, define skills/AGENTS.md and CLAUDE.md files for shared learning, and intervene on edge cases or drift.
  • Leaders: Align outcomes, not velocity; measure by intent accuracy meaning how precisely the shipped product matches the vision. This requires clarity of vision.
  • All: Curate guardrails against hallucinations, ensure governance, and build agent-accessible tools.

Success shifts from "how many points burned" (Is there truly any value to this metric?) to sustained architectural health and business alignment.

Agentic Principles (Beyond the 4 Values)

We follow these principles:

  • Prioritize agent autonomy with human-defined guardrails and observability.
  • Build agent-first codebases: modular interfaces, fast tests, and MCP or skill-compatible structures for reliable orchestration.
  • Embrace continuous learning: Maintain shared "mistakes files" and trajectory logs for agent improvement. This aligns with compound engineering (Plan → Work → Review → Compound) from Every, where agents capture structured learnings from each task. Upcoming tools like ThinkGit extend this by versioning full AI conversations in Cursor or VS Code, making it easy to search, visualize, and compound insights across sessions—turning agents into self-improving teammates with institutional memory.
  • Measure by outcome alignment and system reliability, not proxy metrics. Proxy metrics (story points, velocity) are indirect surrogates; true measures track real business impact (e.g., feature adoption rates, revenue contribution, user satisfaction) and system health (e.g., uptime, architectural drift scores, hallucination/error rates, mean time to recovery).
  • Integrate multi-agent swarms, worktrees, and parallel agents for complex tasks while scaling human oversight intelligently.
  • Govern rigorously using security-by-design architecture to mitigate dual-use risks (capabilities that can be repurposed for harm, e.g., code agents generating exploits, malware, or misinformation workflows) and hallucinations. Anthropic's 2026 report emphasizes this as essential for production-scale agentic coding.

Transition Roadmap: From Agile to Agentic

Phase Legacy Agile Practice Agentic Shift Key Enablers / Examples
Planning Detailed Jira tickets & backlogs Context Capsules with intent + constraints High-level prompts, AGENTS.md files, Claude Skills, ThinkGit
Execution Time-boxed sprints & daily standups Continuous flow via agent swarms Multi-agent frameworks (e.g., LangGraph, PydanticAI)
Review & Validate Manual code reviews & retros Automated validation + System Pulse Agentic peer review, trajectory observability
Measurement Story points & velocity Intent accuracy & architectural health Outcome metrics, drift detection

Challenges certainly remain. Over 40% of agentic projects risk cancellation by 2027 without strong governance (Gartner). Hallucinations, coordination failures, and legacy integration demand hybrid approaches—neuro-symbolic agents for verifiability, federated swarms for scale.

Adapt your process to the speed and autonomy of your tools, or watch your process become the biggest source of technical debt.

This is v1 of the Agentic Manifesto. Add comments, experiences, and evolve it. I'll post it as well on my github. The agentic era is here. Let's orchestrate our agents.

Top comments (1)

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nedcodes profile image
Ned C

The "Context Capsules" idea resonates. I've been writing .cursorrules and AGENTS.md files for different projects and the pattern is exactly this: define intent and constraints, let the agent figure out implementation details. The less prescriptive the ticket, the better the agent performs, as long as the guardrails are solid.

One tension I keep hitting though: continuous flow sounds great in theory, but most teams still have humans reviewing agent output. That review bottleneck reintroduces batching whether you call it a sprint or not. The real unlock isn't just faster execution, it's building enough trust in automated validation that you can reduce human review to spot-checks rather than gatekeeping every merge.

Curious how you're handling architectural drift detection in practice. That feels like the hardest part of letting agents run autonomously.