Forage Solutions

Roam a solution space with ant colony optimization ideas — send independent hypotheses as scouts, reinforce hopeful paths through evidence, spot diminishing returns, and know when to abandon a strategy and roam elsewhere.

When Use

• Face problem with many plausible paths and no clear winner
• When first path tried not working but alternatives are unclear
• Debug with no clear root cause — many hypotheses need parallel dig
• Search codebase for source of behavior when spot is unknown
• When past solution tries have converged early on suboptimal path
• Complement build-coherence when solution space must be roamed before pick

Inputs

• Required: Problem desc or goal (what are we foraging for?)
• Required: Current state of knowledge (what is already known?)
• Optional: Past paths tried and their outcomes
• Optional: Limits on roam (time budget, tool open)
• Optional: Urgency level (hits roam-exploit balance)

Steps

Step 1: Map the Solution Landscape

Before send scouts, describe shape of solution space.

Solution Distribution Types:
┌────────────────────┬──────────────────────────────────────────────────┐
│ Type               │ Characteristics and Strategy                     │
├────────────────────┼──────────────────────────────────────────────────┤
│ Concentrated       │ One correct answer exists (bug fix, syntax       │
│ (one right fix)    │ error). Deploy many scouts quickly to locate     │
│                    │ it. Exploit immediately when found               │
├────────────────────┼──────────────────────────────────────────────────┤
│ Distributed        │ Multiple valid approaches (architecture choice,  │
│ (many valid paths) │ implementation strategy). Scouts assess quality  │
│                    │ of each. Use `build-coherence` to choose         │
├────────────────────┼──────────────────────────────────────────────────┤
│ Ephemeral          │ Solutions depend on timing or sequence (race     │
│ (time-sensitive)   │ conditions, order-dependent bugs). Fast scouting │
│                    │ with immediate exploitation. Cannot revisit       │
├────────────────────┼──────────────────────────────────────────────────┤
│ Nested             │ Solving the surface problem reveals a deeper one │
│ (layers of cause)  │ (config issue masking an architecture problem).  │
│                    │ Scout at each layer before committing to depth   │
└────────────────────┴──────────────────────────────────────────────────┘

Sort current problem. Distribution type picks how many scouts to send and how fast to switch from roam to exploit.

Got: Clear sort of solution landscape that tells scouting strategy. Sort should feel right to problem, not forced.

If fail: Landscape fully unknown? That itself is sort — treat as possibly distributed and send broad scouts. First round of scouting will show landscape character.

Step 2: Deploy Scout Hypotheses

Make independent hypotheses as scouts. Each scout probes solution space in different direction.

1. Make 3-5 independent hypotheses about problem or its solution
2. For each hypothesis, set one cheap test — single file read, one grep, one specific check
3. Rate first promise by open evidence (not gut feel)
4. Send scouts independent: do not let check of hypothesis A hit test of hypothesis B

Scout Deployment Template:
┌───────┬──────────────────────┬──────────────────────┬──────────┐
│ Scout │ Hypothesis           │ Test (one action)    │ Promise  │
├───────┼──────────────────────┼──────────────────────┼──────────┤
│ 1     │                      │                      │ High/Med/│
│ 2     │                      │                      │ Low      │
│ 3     │                      │                      │          │
│ 4     │                      │                      │          │
│ 5     │                      │                      │          │
└───────┴──────────────────────┴──────────────────────┴──────────┘

Key rule: scouts check, they do not exploit. Goal is quick signal on each hypothesis, not deep dig of first one that looks hopeful.

Got: 3-5 independent hypotheses with cheap tests set. No hypothesis deep roamed yet — this is breadth-first pass.

If fail: Fewer than 3 hypotheses can be made? Problem is either very tight (concentrated type — good, scout aggressive) or grasp too shallow (read more context before hypothesize). Hypotheses not independent (they are all variations of same idea)? Roam too narrow — force at least one hypothesis that clashes with others.

Step 3: Trail Reinforcement — Follow the Evidence

After scout results return, reinforce hopeful trails and let weak ones decay.

1. Review scout results: which hypotheses found backing evidence?
2. Strong evidence found → reinforce trail: invest more dig effort here
3. No evidence found → let trail decay: do not dig further without new signals
4. Clashing evidence found → mark as inhibition signal: actively avoid this path
5. Watch for premature converge: if all effort flows to first trail reinforced, force one scout into unexplored ground

Trail Reinforcement Decision:
┌───────────────────────────┬──────────────────────────────────────┐
│ Scout Result              │ Action                               │
├───────────────────────────┼──────────────────────────────────────┤
│ Strong supporting evidence│ REINFORCE — deepen investigation     │
│ Weak supporting evidence  │ HOLD — one more cheap test before    │
│                           │ committing                           │
│ No evidence               │ DECAY — deprioritize, scout elsewhere│
│ Contradicting evidence    │ INHIBIT — mark as dead end           │
│ Ambiguous result          │ REFINE — hypothesis was too vague,   │
│                           │ sharpen and re-scout                 │
└───────────────────────────┴──────────────────────────────────────┘

Got: Clear priority of trails by evidence, not preference. Strongest trail gets most focus, but at least one alternative stays alive.

If fail: All scouts return empty? Hypotheses were wrong — not the path. Reframe question: "What assumptions am I making that could be wrong?" Make new hypotheses from different angle. All scouts return strong signals? Problem may be distributed (many valid answers) — switch to build-coherence for path pick.

Step 4: Marginal Value Theorem — Know When to Leave

Watch yield of current path. When info gained per unit effort drops below average across all paths, time to switch.

Marginal Value Assessment:
┌────────────────────────┬──────────────────────────────────────────┐
│ Signal                 │ Action                                   │
├────────────────────────┼──────────────────────────────────────────┤
│ New information per    │ CONTINUE — this trail is productive      │
│ action is high         │                                          │
├────────────────────────┼──────────────────────────────────────────┤
│ New information per    │ PREPARE TO SWITCH — squeeze remaining    │
│ action is declining    │ value, begin scouting alternatives       │
├────────────────────────┼──────────────────────────────────────────┤
│ Last 2-3 actions       │ SWITCH — the trail is depleted. The cost │
│ yielded nothing new    │ of staying exceeds the cost of switching │
├────────────────────────┼──────────────────────────────────────────┤
│ Information contradicts│ SWITCH IMMEDIATELY — not just depleted   │
│ earlier findings       │ but misleading. Cut losses               │
└────────────────────────┴──────────────────────────────────────────┘

Key: factor in switch cost. Moving to new hypothesis means loading new context, which has cost. Do not switch for marginal gains — switch when current trail clearly depleted.

Got: Deliberate pick to continue or switch by yield check, not habit or frustration. Switches are evidence-based, not impulse-driven.

If fail: Switch happens too often (oscillation between hypotheses)? Switch cost being under-valued. Commit to current trail for N more actions before recheck. Switch never happens (stuck on one trail despite dropping yield)? Set hard cap: after N unproductive actions, switch no matter sunk cost.

Step 5: Adapt Strategy to Results

By forage results, pick right next phase.

1. Most scouts empty, one trail weak → problem likely misframed. Step back and reframe: what question should we be asking?
2. One strong trail, others empty → concentrated problem. Exploit strong trail with full focus
3. Many competing trails → distributed problem. Apply build-coherence to pick among them
4. Clear winner emerging → move from roam to exploit. Drop scouting budget to 10-20% (keep one scout active for alternatives), commit main effort to winning path
5. All trails exhausted → solution may not exist in current search space. Expand: different tools, different assumptions, ask user

Got: Strategic pick about next phase that follows logically from forage results. Pick should feel like conclusion, not guess.

If fail: No strategy feels right? Forage has shown real uncertainty — and that is valid outcome. Say uncertainty to user: "I roamed N paths and found X. Most hopeful is Y because Z. Shall I pursue it, or do you have extra context?"

Validation

• Solution landscape sorted before scouting started
• At least 3 independent hypotheses made and tested
• Scout tests cheap (one action each) and independent
• Trail reinforcement based on evidence, not preference
• Marginal value checked before commit to deep dig
• Strategy adapted to results rather than follow fixed plan

Pitfalls

• Premature exploit: Dive deep into first hypothesis that shows any promise without scouting alternatives. This is most common fail — first good idea is often not best idea
• Endless scouting: Make hypotheses endless without ever commit to one. Set budget: after N scouts, commit to best trail no matter what
• Non-independent hypotheses: "Maybe it's in file A" and "maybe it's in file B, which is imported by file A" are not independent — they share assumptions. Force real diversity of path
• Ignore inhibition signals: When evidence clashes with hypothesis, let it go. Keep invest in contradicted trail because of effort already spent is forage equivalent of sunk cost fallacy
• Scout without record: If scout results not logged, later scouts will repeat earlier work. Brief note what each scout found before move to next

See Also

• forage-resources — multi-agent forage model that this skill adapts to single-agent solution search
• build-coherence — used when forage shows many valid paths that need check
• coordinate-reasoning — manages info flow between scout hypotheses and exploit phases
• awareness — watches for premature converge and tunnel vision during forage