Authoring speed ≠ delivery
Agents draft faster than the pipeline can absorb. Review becomes the bottleneck, rework creeps up, and shared truth still lives in chats and heads. Throughput looks busy while delivery velocity stalls.
New methodology
IMPACT - Agentic SDLC, embedded into your stack.
An operating layer for agentic engineering - we wire Intent, Memory, Policy, and Checks into your tickets, repos, and CI.
Your teams have AI tools. Delivery still stalls.
R&D leaders need competitive velocity without giving up quality. Ad-hoc agent adoption usually creates AI tax - review bottlenecks, rework, and seniors drowning in verification while throughput looks busy - in three places:
No standards, uneven outcomes
Everyone picks their own tools, prompts, rules, hooks, and habits. Techniques stay tribal, the verification loop has no shared Policy, and quality becomes a person problem instead of a system property.
Serial bottlenecks where parallel should win
Agents can run work in parallel, but teams still jump between tabs and lose context on every handoff. Without one living shared context and Policy contract, more agents just multiply the AI tax.
Founder & Creator of IMPACT
Hi, I'm Adir
I've operated at both ends of engineering - startups and global enterprise, while shaping Israel's engineering community.
Startup to enterprise
Early engineer at Eureka Security through high growth and acquisition by Tenable - then led engineering there, shipping complex systems under security, compliance, and quality pressure.
Community Leadership
Co-founder and co-host of "לא טכני ולא במקרה" - one of Israel's largest eng communities and a Geektime Podcast Contest 2025 3rd-place winner. I'm also a frequent speaker on dev conferences and meetups.
I built IMPACT to kill the AI tax: agents that speed up authoring while seniors drown in review and rework. You get startup speed, cyber-grade discipline, and real AI SDLC practice - wired into your team workflow.
Early pilot
We're installing IMPACT with a small group of early teams.
Those first engagements are at pilot pricing - a lower entry while we prove the scorecard together. If it might be a fit, leave a note and we'll follow up.
IMPACT feedback loop
Where context, standards, and agents reinforce each other
The agentic SDLC loop
Intent
Work is anchored to tickets - living specs PMs and agents maintain together.
A ticket is the contract between product intent and engineering execution. When a PM shapes a feature with an agent, product decisions land on the ticket - scope, constraints, open questions, acceptance signals. When an engineer picks up work, the agent loads Intent first, not a pasted prompt from yesterday's chat.
- Versioned in your ticket system (Jira, Linear, or equivalent)
- Front door to every agent session
- Without Intent on the ticket, every run is an interpretation - and agents accelerate divergence
How IMPACT connects
Choose a use case to see how IMPACT connects.
Developer
Work starts on Intent. The agent loads the ticket, retrieves Memory, runs inside Policy, and only then codes - so the session is not a blank prompt.
Pilot Launch
In 4 to 6 weeks, we embed with your team and wire IMPACT where you already work.
Week 1
Audit
Map your stack, tools, and how the team already uses agents - clear gaps and a golden path to wire IMPACT into.
Week 1
Kickoff
Walk through findings and lock scope, owners, and prerequisites - everyone aligned on the plan and who owns what.
Week 2
Scaffold
Set up living shared context, Policy, and Checks on the golden path - agents can pull context, Policy has a skeleton, CI actions are in place.
Week 3
First end-to-end run
Take a real product ticket through the full agent workflow - one live run of Intent → Agents → Checks end to end.
Week 4–5
System hardening
Tighten context, close Policy gates, and capture the scorecard baseline - Policy fails closed with a Velocity + Quality before/after.
Week 5–6
Hand off
Leave you with evidence - your team runs the golden path without us, then decide on next steps: Expansion or Retainer.
What you own at hand-off
Concrete artifacts on your systems - not a slide deck.
- Living shared context (markdown vault in git + retrieval)
- Thin Intent bridge between tickets and agent sessions
- Policy pack: rules, skills, hooks, ADR gate, and CI gates that fail closed
- Checks path on the golden-path coding surface
- Velocity + Quality telemetry baseline (before/after pack)
- Operating runbook: compile / promote ritual so Memory stays sharp
Pilot / Launch scorecard targets
What Success targets look like
What we aim to move on a pilot. Velocity has to improve, Quality has to improve.
Pilot target pillar
Velocity
Delivery moves - without the AI tax bottleneck.
>0%
Get from a clear ticket to review-ready work much faster.
<0%
Spend less time stuck in review queues, rounds, and cleanup.
<0%
Human steps outside the defined approval gates. This count shrinks toward only those gates.
Pilot target pillar
Quality
The gates mean something - even as you ship faster.
<0%
Less bugs slip through after the gates we set - approved the outcome.
>0%
Agents clear the gates on the first try more often.
<0%
Less rework and fewer reverts after merge.
Questions leaders ask before they embed.
Pilots, fit, security, Memory, Policy, tools, and how you know the scorecard is working.
Schedule a pilot
Interested in a pilot? Leave your details and we'll get back to you.